AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation
Authors: Teng Li
Liwen Zhang
Youcheng Zhang
ZijunHu
Pengcheng Pi
Zongqing Lu
Qingmin Liao
Zhe Ma
Venue: NeurIPS 2024 poster (2024)
Keywords: Radar Semantic Segmentation
Adaptive Peak Convolution
Primary area: machine_vision
TL;DR: A more robust novel convolution operator tailored for radar signals.

Abstract: Deep learning-based radar detection technology is receiving increasing attention in areas such as autonomous driving, UAV surveillance, and marine monitoring. Among recent efforts, PeakConv (PKC) provides a solution that can retain the peak response characteristics of radar signals and play the characteristics of deep convolution, thereby improving the effect of radar semantic segmentation (RSS). However, due to the use of a pre-set fixed peak receptive field sampling rule, PKC still has limitations in dealing with problems such as inconsistency of target frequency domain response broadening, non-homogeneous and time-varying characteristic of noise/clutter distribution. Therefore, this paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented. Through experimental verification using various real-measured radar data (including publicly available low-cost millimeter-wave radar dataset for autonomous driving and self-collected Ku-band surveillance radar dataset), we found that the performance of AdaPKC-based models surpasses other SoTA methods in RSS tasks. The code is available at https://github.com/lihua199710/AdaPKC.

AdaPKC: PeakConv with Adaptive Peak Receptive Field for Radar Semantic Segmentation
Teng Li Liwen Zhang
Youcheng Zhang' Zijun Hu
Pengeheng pi Zongqing Lu² Qingmin Liao
Zhe Ma
Intelligent Science and Technology Academy of CASIC 2Shenzhen International Graduate School, Tsinghua University liteng216mails. tsinghua. edu. en" 1wzhang91611126. com"
liaoqmttsinghua.cr www.sinnhuauuuu.u...mmm edu.
Abstract
Deep learning-basee radar detection technology is receiving increasing attention in areas such as autonomous driving, UAV surveillance, and marine monitoring. Among recent efforts, PeakConv (PKC) provides solution that can retain the peak response characteristics of radar signals and play the characteristics of deep convolution, thereby improving the effect of radar semantic segmentation (RSS). However, due to the use of a pre-set fixed peak receptive field sampling rule, PKC still has limitations in dealing with problems such as inconsistency of tar- get frequency domain response broadening, non homogeneous and viryyng characteristic of noise/clutter distribution. Therefore, this paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, novel fine-tuning technology to further boost the performance AdaPKC-baat RSS networks is presented. Through experimental verifica- tion using various real- -ea-ureeee radar data (including publicly available low-cost millimeter-wand radar dataset for autonomous driving and self-collected Ku-band surveillance radar dataset), found that the performance of AdaPKC-based models surpasses other SoTA methods in RSS tasks. The code is available at https://github.com/iiuu
http:///..ooomiuooa////////0 199710/AdaPKC
Introduction
As common remote sensing device, radar exhibits superior robustness in complex environments (e.g., varying weather and lighting conditions) compared to cameras, and it is more cost-effective and resilient in extreme weather scenarios compared LiDARs. Benefiting from the physical advantages of radar sensors and the powerful capabilities of deep learning techniques, modern deep learning-based radar signal interpretation has become a hot research topic in the field of radio frequency detection technology. It has been extensively explored in autonomous driving 300 [9 etc. Considering the similar dense representations between radar frequency maps and optical images, most of these works directly transfer convolution networks or modules developed for optical signals to radar perception tasks, such radar object detection (ROD) and radar semantic segmentation (RSS), and they have achieved impressive performance. Nevertheless, without specific design for the inherent characteristics of radar signals, these approaches fail to fully liberate the potential of deep learning techniques.
34
UAV surveillance 17, sea monitoring (7
Recently, PKCIn-Net 322 introduced an innovative convolution operator named PeakConv (PKC), tailored for the efficient analysis of radar signals, and this operator seamlessly integrates the advan-
'Equal contribution. 'Corresponding author. This research is supported by Young Science Foundation of National Natural Science Foundation of China (No.62206258).
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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(A) Synchrantedd camera Image the first franse
(b) Radar Frequence
Figure 1: The illustration of variations in target signature and interfering signals in radar frequency map. The first row illustrates the variations of the same target across temporally consecutive frames in the range-Doppler (RD)-amplitude 3D representation. The second row demonstrates the disparities of different targets in the same frame, well the same target across different frames, in the range-angle (RA) 2D representation. Cyan and yellow rectangles represent target areas, illustrating the variations of target signature with dimensions, categories, and time, etc. Red ellipses indicate prominent interfering clutter, while purple ellipses represent clutter undergoing significant changes.
tages of classic radar detectors 22 with common convolution networks 19 For radar signals, the frequeney responses of objects comprise target echoes and interference, and share a distinct peak-shaped pattern, thus most classic radar detection methods [22 10
23 build peak detection algorithms upon constant false alarm rate (CFAR) criteria. Extending from cell averaging- -FFR (CA-CFAR) 22 PKC explicitly embeds similar band-pass peak enhancement mechanism in standard convolution operator for better characterising target signatures in radar signals. Concretely, following the guard-reference policy of CA-CFAR, first estimates interfering signals with the center unit/cell and reference units outside predefined guard bands. Then, with estimated interference, finishes noise suppression for each cell under test (CUT) in feature space and enhances peak frequency response associated with objects of interest.
25
Despite its superior suitability for radar data than alternative convolution operators exists even greater potential for PKC to learn peak frequency response of radar signals. Research on CFAR detectors [10] 25) 23 reveals that there exist significant variations in target signature and associated interference within radar signals, rendering the predefined reference cells in CA-CFAR inadequate for precisely locating interfering signals, and this limitation can also be observed in PKC. To provide a clearer depiction, let us delve deeper into these variations present within radar signals, radar tensors are generated
33 there
as illustrated in Fig. [ On the target side, since multi-dimensionne through sequence of cascading fast Fourier transformations (FFTs), target signatures along different dimensions exhibit distinct degrees of frequency response tailing (broadening). Additionally, the broadening degrees of different instances would also be influenced by target categories or states, iee. the relative distances, azimuth or velocity from the radar. On the interference side, the noise or clutter distribution commonly exhibits non-homogeneous and time-varying characteristics. However, since the PKC kernel always gathers the reference units at fixed locations for noise estimation, i.e., the predefined peak receptive field (PRF), the dynamic variations in both targets and interference degrade its performance. In short, the fixed PRF essentially limits the learning ability of PKC, thus, it hinders the RSS model from obtaining better performance.
Motivated by the adaptive selection of reference cells in classic CFAR detectors [10 25 23 this
work we introduce two novel lata-vdaptive band-pass filtering mechanisms aimed at upgrading PKC to adaptively adjust its PRF for each CUT in a data-driven manner, namely adaptive PeakConv (AdaPKC). Concretely, both versions of AdaPKC first measure the correlation between CUT and its

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alternative reference units in high-din dimensionn feature representations, then select proper reference units and integrate them seamlessly with PKC effectively take care of the fluctuating dynamies of radar signals. The main contributions of our work are:
We present the first attempt specially tailored for radar signal processing to dynamically adjust the receptive field for convolution operators. Concretely, we propose a novel updated version of PKC, termed as AdaPKC, which can adaptively adjust the PRF (or reference units) at cell-level granularity. And two different implementation versions are provided, which both exhibit enhanced flexibility and robustness in handling fluctuating radar signals compared to original PKC.
To better release the learning ability of AdaPKC, a fine-tuning technology with thresholding on-line switch is presented. With such technology, the same AdaPKC-basee C-bseeed model can even achieve better performance with less computational cost.
To verify the effectiveness of AdaPKCs, quantitative and qualitative experiments are conducted on various real-measured large scale radar datasets including CARRADA (20) collected from a low-cost FMCW (+= 77GHz) radar in autonomous driving scenario and self-collected dataset recorded from Kurz-under (Ku) band (=e 17GHz) radar for UAV surveillance and monitoring. Results show that AdaPKC-based models achieve SoTA RSS performance and our fine-tuning strategy further brings visible improvements, verifying the scope of application of AdaPKCs.
2 Related Work
Receptive field (RF) adjustment. RF is crucial for modern deep convolution models, affecting the granularity of modeling primitives, computation architecture and their representation capabilities, etc. The rational use of RF can directly improve the representation ability of the models, e.g. enlarging the scope [3I, multi-scale modeling [2], and dynamically changing shapes 5 33 Beyond that, the concept of RF has also been applied to Transformers [6]. These methods are proposed for vision tasks and have achieved significant results. However, compared with conventional convolution, the improvement is not satisfactory enough on radar signals. Recently, by fully considering the characteristics of radar signals, the concept of PRF has been proposed, which makes the original convolution have the ability of band-pass filtering and noise suppression (32]. However, the bandwidth of filtering is pre-set, which hinders the adaptive ability of PKC to radar data. To this end, this paper attempts to study a data-driven PRF adjustment method, and introduces the concept of adaptive PRF (AdaPRF), so to further improve the RSS performance. Considering that the adaptive adjustment of the suppression (guard) bandwidth and the non-suppression (reference) units is essentially a dynamic adjustment of RF, and from this point of view. the content studied in this paper is related to deformable convolution (DefConv) [5] 333. Unfortunately, the dynamic RF technology of DefConvs cannot solve the problem in hand, for the following reasons: i) Differences of signaling mechanism. DefConv uses the visual prior information that the target is visually deformed geometrically, which cannot be directly corresponded to the radar signal. ii) Mismatched prediction method. DefConv generates new RF through prior prediction, i.e., the regular RF is still used to infer the sampling point outside regular RF. However, in radar signal, the interference (noise/clutter) with large entropy, is often difficult or even impossible to predict. At present, a better way is under the premise of observation, i.e., posterior measurement or statistics. iii) Different mechanisms of representation. Noise suppression is not required to be considered by DefConv, thus it does not need to distinguish between the center unit and surroundings during calculation. To this end, novel adaptive RF adjustment method is required.
Radar semantic segmentation. Benefiting from the reliable perceptual capabilities, convolutional neural networks (CNNs) play an indispensable role in existing RSS networks for radar frequency maps processing, whether in pure CNN models [13 S 19
32 or transformer-assiiseed CNN mod-
els (4
12 6
RSS-Net 313 utilizes fully convolutional neural network with encoder-decoder
structure to recognize targets in radar scans, and it incorporates an atrous spatial pyramid pooling
(ASPP) 2 module gather multi-scale spatial information. RAMP-CNN [8] employs parallel branches to extract features from multiple views and adopts 3D convolutions to better capture temporal information. TMVA-Net [19] leverages these techniques to develop multi-view, RSS model, which is capable of making semantic predictions across multiple views simultaneously. T-RODNet [12] integrates Swin Transformer [16] modules into a CNN-based RSS model to strengthen its modeling capability. TransRSS 34 and RaansRadar [6] introduce attention blocks into the multi-view feature fusion stage to enhance the fusion effectiveness. Recently, as the first fundamental convolution opera- tor tailored for radar signal processing, PKC [32 is proposed. Compared to Dilated Convolution [3]
3

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and Deformable ones B3]. PKC demonstrates superior RSS performance. However, it is inherently constrained by its fixed peak receptive field, posing challenges achieving consistent interference (noise/clutter) suppression under the dynamic and time-varying nature of radar signals. By contrast, our AdaPKCs overcome this limitation well by implementing novel data-adaptive band-pass filtering mechanisms.
3 Method
In this section, the proposed two versions of AdaPKC with different AdaPRF mechanisms are first introduced in $ 3.T Then, the proposed fine-tuning strategy further uncover the potential of AdaPKC-based RSS models is presented in $ 3.2] We design these models using both multi-
view [19 32 and single-view frameworks, which are comprehensively described in Appendix A.2
and A.3 for saving space.
3.1 AdaPKC
3.1.1 AdaPKC PKC w/ Metric-based AdaPRF
To ensure the reliability of estimating the proper unit-level PRE, ie., AdaPRF, for AdaPKC in radar signals, establish the estimation process in posterior way: first define of candidate PRFs within the neighbourhood of the center unit, aligning with the local peak response of targets and the local scanning process of convolution, and then design measuring criterion to evaluate these PRFs, estimating AdaPRF primarily occupied by interfering signals. Motivated by classic CFAR detectors [10 25 23 we first demonstrate how to estimate AdaPRF in an explicitly measuring way, referred to metric-based AdaPRF (AdaPKCS). To illustrate the mechanism of AdaPKC begin by defining the search space of alternative PRFs. Reviewing the PRF definition in previous work we can see that, the PRF for center unit x. encompasses x. itself and a set of sampled
reference units {x, Jr=1, N. and the area of reference units is governed by horizontal- and vertical- symmetry guard bandwidth bo {69 bg) and reference bandwidth ba A {b.. by as illustrated in Fig. (a). Following this definition, the PRF adjustment corresponds precisely to the adjustment of the reference unit set, thus we can define the PRF search space by defining the candidate sets of reference units with the adjustment ranges for the guard bandwidth and reference bandwidth. Given that adjusting the reference bandwidth leads to a drastic change in the number of sampled reference units compared to adjusting the guard bandwidth, in AdaPKC we keep anytime-fixed 1, yy = 1) and denote the set of K guard bandwidth candidates as Ω2G A [b)) = I bc 8G BG ly b < 8G mmax Ig). generating K sets of reference units as max [x:
bR A
{UR
min Jz
1= correspondingly. As a result, AdaPRF estimation AdaPKC is equivalent to selecting an appropriate reference unit set from these candidate sets for each CUT (or center unit), as illustrated in Fig. (b).
{[x
(4) No K
For better explanation, we divide the observed radar signals into three subsets: i) signals reflected directly from a target, S; ii) the target-interfering noise, i.e., the noise coupled with the signal that partially leaks out of the target, Sya; and iii) the target-independent noise, Sn- practice, part that from S., really causes misjudgment. Therefore, classic CFAR detectors focus filtering out such noise either by the extreme value [10 or the median value (23 in amplitude domain of signals. Motivated by this idea, our AdaPKC centers its attention on collecting reference units predominantly occupied by such noise.
the
However, as learnable module, AdaPKC needs to process the representation tensors of radar signals, implying that the measurement of the reference units should be conducted on feature space. For some CUT X s5s;; W) and its candidate reference unit x,, = ri(s'; W), where s E S. s' E S, U.S S, and w(:: W) E RC denotes a convolution layer with shared weights, W. Then, A should be responsible for transforming these features into metric space that explicitly delineates their correlation with the target. This transformation is achieved by utilizing the inner product of representations between CUT and its candidate reference unit, i.e., X,X,T. sharing a similar spirit with the matched filter concept in conventional radar signal processing (2 and attention in (26).

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Iaput feature map
K candidate PREs
Estimation criterion
AdaPRF
Relerenze handwinkk 5 (B..))
Marii
Seleciie
KxfloW
Goard bosdisiitt (5:45
Blayy Klay +1) Blay Raa
PRF
AdaPRF estimation AdaPKc!
Call Under Text (CLTT),
Sampled refervace unil,
Geard unit
Reference unit under confirmation
The illustration of AdaPRF in AdaPKCS (a) illustrates the definition of PRF in PKC, whose area is governed by the reference bandwidth ba and guard bandwidth bo; (b) describes the estimation process of AdaPRF in AdaPKC including denoting K candidate PRFs for each CUT, translating these PRFs into metric scores [ and finally selecting an appropriate PRF as the AdaPRF with these metric scores.
Figure 2:
Under this definition, these measures exhibit the following statistical properties,
E (1wbss; w)ll),
if s' S; 8 E S.n s Sn
E (x.x,)
E (1wbss' W)l2), if
(I)
0,
if
where, EE()) is the expectation and || ||2 denotes the L2 norm. From Eq. (T. can see that the inner product transformation assigns three statistical boundaries to x, from Sp. Spo and Sn for s' E Sea- the expectation E (x,x,) consistently exhibits smaller value than case for s' E S, and larger value than s' Sn, with notable separation between their respective magnitudes. This attribute significantly serves to facilitate the subsequent localization of reference units from eegeetinterfering noise.
Then elucidate the process of translating the K available sets of reference units (or PRFs) into the previously discussed metric space. Since different sets may comprise varying numbers of units, we uniformly sample N (16 by default) units as representatives,
illustrated in Fig. (b). For the center
unit x. and its & reference unit set {x
(6) N let R2 = fxe: {x( (i denote its corresponding PRE. Then the correlation value (or metric score), for the Etth PRF w.r.f. X. is formulated
N
Ex
Σ
xox
(t) /C),
(2)
where, C is the feature dimension; o())
(:) is the sigmoid function which normalizes & (0, 1).
With the correlation values = (Ex [ for all alternative PRFs, we can select the appropriate PRF Ri, which effectively encompasses target-interfering noise. view of the attribute presented Eq. 1 employ the maximum value of the first-order gradient of 3 the selection criterion and please refer to Appendix B. for the detailed analysis. Then, we have final selection strategy as follows,
R
R+1
k=ki
{xe}
P/k 1i s.t., k arg max {9 (sort (E))).
where arg max operator retrieves the index corresponding the maximum value in 9 [sort (E)]; 9 is the difference function; sort is the descending operator. After obtaining Rt, AdaPKC performs a convolution operation similar to PeakConv, detailed in Appendix A.I
3.1.2 AdaPKC": PKC w/ Learning-based AdaPRF
In $ measuring criterion is established in Eq. based prior knowledge of radar signals, providing non non-paramttric way to achieve AdaPRE. Differing from AdaPKC AdaPKC" learns to

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Input feature
Optimal guard bandwidth
of
AS
4F.42.E)
Ix CCnn
Examupl candidate
Estimaaiin aetwork optinsal guard bandwidth
(C
Sampled
sigmald
Figure 3: The illustration AdaPRF in AdaPKC (a) illustrates an example of candidate PRFs in AdaPKC where the guard bandwidth bo is in a quadruple form; (b) describes the flowchart of the optimal guard bandwidth estimation network, which consists of two parallel branches that sample representative points in their corresponding directions and then automatically measure and select the optimal guard bandwidth.
build the criterion in task-driven manner, employing small network to estimate AdaPRF, i.e., parametric way. Thus, given center unit and its K candidate PRFs, /R the natural way to locate AdaPRF R!, is to define a function f(; 0) with learnable parameters 0, which is used to produce the likelihood each R2 being Ri, then we can have
Rt Rgt, where k arg max &
{[S(Ra; 0)
(4)
At a rough glance, there is no obvious difference between Eq. and Eq. However, AdaPKC involves joint optimization of estimation network parameters (i.e., 0) and segmentation model parameters. The use of arg max operation will lead to the loss of gradient information of 6, as a result, the optimization of the estimation network cannot be driven by the segmentation task. To this end, we transform the discrete estimation problem into a continuous form. Firstly, following the setup of anytime-fixe fixed R & { = 1, b = 1} in AdaPKC estimating Rt can be equivalently translated into estimating the optimal guard bandwidth b Subsequently, to ensure that the estimation of b retains gradient information, the estimation network is designed to generate the continuous- valued b instead of the likelihoods for alternative guard bandwidths. Finally, the derivable linear interpolation is used to associate the ciciinuous-vllleee bt with discrete spatial coordinates.
Concretely, given input feature map X RCCHx our AdaPKC" is responsible for obtaining Gt H.W
an appropriate guard bandwidth B (b, /tl, 44 For enhancing the expressive capability of AdaPKC, the horizontal- and vertical-symmett be design in the original PRF is extended quadruple form, bc {66, 6,6 88, that the shape of PRF would enjoy free change in four directions, i.e., top, bottom. left, right, illustrated in Fig. (a), resulting in more diverse band-pass filters. As shown in Fig. (b)),, we use small network with two paralleled cony blocks, Hz CxC
Hx and g Vie (-): RCCHxX 2x estimate bo Rd for each unit of X in horizontal (+->) and vertical (fL) directions, respectively. To ensure the directional consistency, the horizontal branch possesses kernels with a size of x (2 1), and the kernel size of the vertical branch is (2b + 1) 1. correspondingly. All four directions have the same lower bound, &c
and the same upper bound, &G b
by default.
OONN W (N= 16 by default) can be obtained by the Then, for each x,, its FdaPRF Rt = (Xe, (x following two steps:
Step Guard bandwidth estimation:
B Gf
(6G (6max
( - (x)( @gg (x)())) + b 1,
(5)

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where, each b in Bet is the learned guard bandwidth for x of the spatial coordinate (h, w); logether with constant b min the sigmoid activation (+) can modulate the input values to (0, ig U mia): : 3()) and @ denotes the BatchNorm and Concat operator, respectively; 1 g Hx is all-one cube.
Step 2. Reference unit sampling: for better illustration, we first define the default PRF with guard bandwidths equal to in [32] as Ro [xe, (xx ) which is shown in Fig. (a). let (f) wy denote the spatial coordinate of the current center unit x, P. (he, we) and p -(hp)
and one of its reference unit x² in Ro respectively. Then we split {x,
(9) 1 into four subsets as )
MM according the four directions, where M = N/4. (xx j==1. {x({ j==1. fx122 JM j==1 and (x( 11
as indicated by the braces drawn in Fig. (a). By using but obtained from Step 1, we can sample RT {xe, { } through linear interpolation, exemplified in Fig. (a). Taking the top direction as an example, each (t)t can be obtained by
51 1 - b [ -1o -1,10/7 + (0 -64 x, 169t -1,,0 G)
(6)
Sampling values for the rest directions and back- back-propagan propagation of gradients in linear interpolation are demonstrated in Appendix B.2
3.2 Fine-tuning AdaPKC with Thresholding On-line Switch
To further release the learning ability of AdaPKC, a fine-tuning strategy is proposed. To improve interpretability, we focus mainly on optimizations for the explicit AdaPRF estimation version, i.e.,
AdaPKCS The motivation comes from two perspectives. i) Model confidence: the representation ability and confidence of the model gradually improve with training, hence, during early training phase the less representative features may result in unreliable metric scores calculated in Eq. 2 consequently affecting the selection of AdaPRF and misleading the subsequent learning process. i) Data sparsity: AdaPKC is responsible for the PRF adaptation for both background units (x. S,) and target units (x-= E Si). However, due to the sparsity of target occupation w.r.t. the radar detection range, most units in the input feature map are background units, thus the heavy class-imbalance hinders the model's s concentration on target points and their AdaPRF optimization.
To address the above issues, we propose to fine-tune AdaPKC with a thresholding on-line switch (FiTOS), and please refer to Appendix with pre-defined PRFs is used to initialize the AdaPKC-based model to be optimized, that AdaPKC can have a warm-start before PRF adjustment. Thus, the risk of obtaining unreliable metric scores is greatly reduced. Secondly, considering that the majority of background units are occupied by locally similar noise, i.e., their monotonic correlation/metrie curves, sort(E), are relatively flat, FiTOS introduces confidence threshold to filter out these background units in spatial dimension on-the-fly. Specifically, if the steepness of the metric curve, i.e., max [9 [sort (E)]}, is below the threshold т. then the corresponding unit is considered a background unit, and we retain the initial PRF, i.e., switch off PRF adjustment. Otherwise, we adopt the newly estimated AdaPRF. i.e. switch on PRF adjustment. As a result, the PRF selection criterion in Eq. (3) is modified as follows,
a visual illustration. Firstly, pre-trained PKC model
RI A Rx kmntt (xx) U (x(0
(0) 1.W
arg max (sort (E]]) if max(g (sort (E)]) >
s.t.,
hp
otherwise
where ko denotes the index of pre-defined PRF in the pre-trained model.
4 Experiments
To verify the effectiveness of our methods, we conduct quantitative and qualitative experiments on two public multi-view radar datasets and our self-collected single-view radar dataset. For simplicity of notations, AdaPKC$ -based and AdaPKC -basd multi-view RSS model is denoted as AdaPK -Net and AdaPKC -Net, respectively. The proposed single-view baseline model is named KuRALS--N Additionally, we append "FiT" in the upper right corner of the models to indicate the use of FITOS.
7

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4.1 Datasets and Training Setups
CARRADA 20] dataset is recorded by a low-cost FMCW radar millimeter wave band ( 77GHz). It comprises camera-radar synchronised multi-view radar recordings in various scenarios, and contains four categories of objects: pedestrian, cyclist, car and background. The dimensions of provided amge-angle-Dopplee (RAD) tensors are 256 x 256 x 64 and support multi-view (RA and RD views)
RSS task. The dataset splits are the same as in [32 19. CARRADA-RAC [32 dataset is derived from CARRADA and mainly calibrates the original RA annotations, Appendix D.I for details.
KuRALS dataset is self-collected by Kurz-under band (= 17GHz) surveillance Radar, which is recorded in multiple scenarios, including Aerial vehicles, Land targets and ships on the Sea surface. Different from other public datasets, e,g. CARRADA (20) and CRUW [29], KuRALS aims at exploring the performance of deep models in the field of monitoring radar, hence offering a greater range field ( 6.4km) and higher Doppler resolution (+s 0.198m/s). The comprised RD tensors are stored as 2D matrices of size 2048 (range) by 128 (Doppler). This dataset contains 9 sequences of radar recordings and there cxist four moving object categories: UAV, pedestrian, vehicle and ship.
Training Setups. Following previous works, all models are evaluated with Intersection over Union (IoU) and Dice scores. These metries are averaged across all classes on the test subset for model performance comparison, yielding mean IoU (mloU) and mean Dice (mDice). Implementation details are presented in Appendix D.2
4.2 Investigation of AdaPKC Mechanism
To investigate the working mechanism of AdaPKCs, a series of comparison analysis is conducted on CARRADA benchmark. We first compare AdaPKCss with PeakConv and manual PRF ad- justment method. Results in Tab. demonstrate that, both versions of AdaPKC exhibit significant enhancements in RSS performance compared to PeakConv, especially in the RA view, and they incur affordable additional computational complexity and inference speed overhead. Considering the severer signal tailing effect in RA view, this suggests that AdaPKC can better handle situations with frequency response ambiguity. For better illustration, we analyze the distribution of guard bandwidths in Appendix [E.1] of PeakConv-based models, as discussed in (32), in this work we undertake a similar exploration experiment within broader range of guard bandwidths. Specifically, different guard bandwidths in range dimension, og E 2,,2,3, 3, 4,5], are tested, while the guard bandwidths in angle and Doppler dimensions are fixed at 1 for controlling variables. To ensure consistent parameter counts under different bandwidth settings, we adopt the same strategy of uniform sampling as in AdaPKC As shown in Tab. 2 presetting a proper PRF globally in a hyper-parameter way can indeed help the PeakConv-based model achieve better performance. However, the manual adjustment way cannot cater to each unit, and the computational cost of traversing the guard bandwidth in all dimensions and directions is quite large. In contrast, AdaPKC completely automates the PRF adjustment, and the adjustment granularity reaches the unit-level. With this unit-level PRF adaptation capability, AdaPKCs demonstrate superior RSS performance compared to the manual adjustment way.
Additionally, since manually adjusting PRFs directly affects the RSS performance
Table 1:
Comparison between AdaPKCs and PeakConv. The best and secondary results are marked with bold and underline, correspondingly. Frame rate is calculated on a workstation with an Intel(R) Xeon(R) Platinum 8255C CPU and a Tesla V100-SXM2 GPU.
Conv Type
#Params
RD View
RA View
GMACs
FPS
@ Frames
mloU
mDice
mloU
mDice
PeakCony
6.3M@5
60.7%
72.6%
43.1%
53.7%
109.8
21.1
C
6.3M@5
61.2%
73.1%
44.1%
55.1% 11
109.8
20.7
AdaPKC"
6.3M@5
61.5%
73.6%
43.6%
54.5%
110.1
18.8
Furthermore, comparative analysis between AdaPKC's and DefConvs (DefConv and DefConv V2) [5] conducted under the same RSS framework. Results in Tab. Fsb.3 that, sharing the similar unit- level dynamic RF adjustment spirit with AdaPKC, DefConvs demonstrate better RSS performance than regular convolution (Conv). However, due to the three missmmismt reasons discussed in $ DefConvs exhibit inferior applicability compared to AdaPKC, highlighting AdaPKC's s suitability for
33
8

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Table 2:
The effectiveness of manual PRF adjustment in PKC. bg represents guard bandwidth range dimension.
Table 3: Comparison between AdaPKCs and DefConvs (DefConv and DefConvV2).
RD View
RA View
Cony Type
#Params @ Frames
View
RA View
mloU
mDice
mIoU
mDice
mloU
mDice
mloU
mDice
Conv
5.6M@5
56.1%
68.0%
37.7%
46.2%
60.7%
72.6%
43.1%
53.7%
DefConv
5.7M @5
58.0%
69.8%
39.1%
48.1%
2
61.0%
73.0%
43.3%
54.1% %
DefConvV2
5.8M
58.8%
70.6%
39.3%
48.6%
3
60.7%
72.7%
42.5%
53,2%
4
59.7%
71.5%
43.0%
53.8%
AdaPKC
6.3M@5
61.2%
73. 1%
44.1 %
55.1 %
5
60.4%
72.5%
42.7%
53.3%
AdaPKC"
6.3M@5
61.5%
73.6%
43.6%
54.5%
achieving adaptive receptive fields in radar signals. For supplementary purposes, we also show the comparison results between AdaPKC and dynamic CFAR detectors 100 25 in Appendix F..
As further explorations, we present an investigation into adaptive sampling strategies for reference band in Appendix E.3] Additionally, Appendix [E.4 we demonstrate training strategy to enlarge the search space of alternative PRFs under fixed resource constraints for AdaPKC
4.3 Comparison with State-of-The-Art (SoTA)
Our methods are further compared with fashionable visual segmentation models and existing SoTA RSS solutions. The quantitative results on the CARRADA benchmark are illustrated in Tab, 4 and the qualitative comparisons are presented in Appendix F.3 Both A and AdaPKC" -Net outperform previous RSS models, including pure CNN models 13
g
19 32 and transformer-
assisted CNN models 12 [). Compared to the baseline model PKCIn-Net, AdaPK exhibits improvements in both RD and RA views, with a particularly notable enhancement in the RA view. A -Net purely relies on task-driven PRF adjustment, achieving a better performance balance between views. Additionally, without consuming extra training resources, our proposed FiTOS strategy further enhances RSS performance of AdaPKCS-pp achieving an overall superiority over AdaPKC -Ntt
Table 4: SoTA comparison CARRADA bench- mark. '-' denotes an unreported and not replicable value. Detailed results by category are presented in Appendix F.T. and the trade-off between per- formance and complexity of these models is illus-
Table 5: Performance comparison CARRADA-
RAC.
Frameworks
RD View
RA View
F.aames
mlol
mDicce
mlol
mDlice
trated in Appendix F2
PKCIn-Net
TMVA-Net
6.3MB5
5.6M@5
60.698
59.7%
69.9%
72.4%
47.3%
46.6%
58.7%
57.9%
AdaPKC Net
6.3e@5
61.6%
73.6%
47.9%
59.3%
Frameworks
RD View
RA View
AdaPKC"
6.3Mes
60.7%
72.75%
48.1%
59.6%
Franses
mioli
mDDlc
mioli
mDlle
AdaPKCf Net
6.3M65
50.8%
72.7%
48.8%
60.5%
FC
134.3M663
54.7%
66.3%
34.5%
40.9%
Table 6: RSS performance comparison on Ku-
U-Ned221
17.3M03
55.4%
68.0%
32.8%
38.2%
RALS. Detailed results by category are presented
DeepLabe3+
59.3M
50.8%
61.6%
32.7%
38.3%
in Appendix F.4
133
10
32.1%
36.9%
32.1%
37.8%
1064MM
27.9%
Frameworks
AParsmss Frames
mioli
mDlice
19
5.6M85
TransRadar
499005
U-Nel
FCN
17.3M@5
134.3M
52.4%
50.45
60.152 1%
19.4%
[-
162.0M64
43.5%
53.6%
DeepLaby3e
59,3M485
52.6%
61.8%
PKCIn-Net
6.3M6:5
60.7%
72.6%
43.1%
53.7%
1.2885
AdaPKC'-Net
6.3M6:5
61.2%
73.1%
44.1%
55.1%
KoRALS-NNE w/
S. w/ AdaPke!
1.28855
1.2M85
57.3%
56.7%
67.2%
65.9%
AdaPKCi-Nel
6.3M
61.5%
73.6%%
43.6%
$4.5%
KuRALS-Nee
1.285
57.8%
AdaPKC"-Na PMP
63Me5
62.1%
74.0%
44.3%
58.5%
KaRALS-N4. Net w/ AdaPket
L.M85
58.2%
67.6%
Results on CARRADA-RAC dataset are shown in Tab. 5 Similar to the trend on CARRADA. the evaluation results exhibit the superior performance of methods than these RSS base- line models. AdaPKC -Net -Net still shows relatively balanced performance improvement in both views. AdaPKC Net demonstrates more pronounced improvement in the RD view, while

---

AdaPKC " shows a greater enhancement in the RA view. We speculate that this might be attributed the collaborative training of different views the multi-view segmentation task.
4.4 RSS Performance KuRALS
To verify the effectiveness of AdaPKCs in other application scenarios, we conduct comparative experiments on KuRALS dataset. Quantitative results shown in Tab. and the qualitative com- parisons are presented in Appendix [.55 Compared to conventional visual segmentation models, our proposed KuRALS-Net offers stronger baseline and AdaPKCs further boost the RSS performance of KuRALS-NGa validating their application potential in surveillance radar detection scenarios.
4.5 Ablation Study for FITOS
Since the threshold plays a vital role in FiTOS, study the impact by testing different values of F from 0.1 to 0.9. The summary results on mDice are shown in Fig. 4 and the corresponding mloU results are presented Appendix F.6 The optimal outcome for AdaPKC$-Net PVT RD and RA view is obtained with = 0.6 and 0.7, respectively. It is observed that the performance of AdaPKCS-Netf -Nt PTT declines when either too large too small. When goes larger, fewer target units adjust their PRFs during the fine-tuning stage, rendering AdaPKC less effective. Conversely, when is small, AdaPKCS tends focus background units with random fluctuations, resulting in training confusion. Compared to PKCIn-Net, A Fvl consistently demonstrates superior- ity under different rS, highlighting the essentiality of PRF adaptation for the original PKC. When compared to AdaPKC AdaPKC - exhibits superior performance with most values of т, demonstrating the efficacy of the fine-tuning strategy. Especially, in RD view, the RSS performance shows significant improvement within a broad range of r. Taking mDice as an example, it consistently surpasses 73.5% for r E [0.5, 0.8], indicating strong level of robustness
AdaPkCf-Net
AdaPKC
PKCIn-Net
74.5%
74.0%
55.5%
95.0%
73.5%
54.5%
73.0%
72.5%
53.5%
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1
0.2
11.3
0.4
0.5
0.6
0.7
03
0.9
F
RD View
RA View
RSS performance of A with different values of {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9). AdaPKC Net and PKCIn-Ne actually corresponds to the case where 7 of AdaPKC$-Net equals to 0 in whole training process and in the fine-tuning stage, respectively.
Figure 4:
Conclusion
This work delves deeply into the convolution operator for radar signals, PKC, and improves upon it. Due to the design of PRF, PKC filters out the reference units corresponding to interfering signals in band-pass filtering manner. Compared to other convolution operators in deep learning, PKC obtains a more robust representation by using the center unit and the reference unit to cancel each other out and then weighted fusion. However, the fixed suppression bandwidth (i.e., guard bandwidth) setting limits the adaptability of PKC to signal diversity. Based on this, we propose a method for adaptive adjustment of the PRF, and provide i.e., $ and $ In addition, to further boost the learning ability of AdaPKC, novel fine-tuning strategy is presented. To fully verify the effectiveness of AdaPKC, different real-measured radar datasets are used for experimental analysis. The results show the superior performance of AdaPKC in RSS tasks.
effective solutions based on metries and learning,
10

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12

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Supplementary Material
A AdaPKC-based RSS Frameworks
A.1 AdaPKC
We formulate the detailed process of how AdaPKC conducts PeakConv on Ri, For the simplicity of notation, we uniformly represent the Rt acquired by the two AdaPKCs as R {x., {x, (1) J
R can be readily incorporated into both PeakConv operators proposed in [32]. including vanilla- PKC and ReDA-PKC. Given ReDA-PKC's enhanced capability utilizing interference and get information for noise suppression compared to vanilla-PKC, we choose ReDA-PKC as the
baseline operator. Therefore, both AdaPKCs share the same structure as ReDA-PKC and differ only in the generation of Ri, Analogous to the formalization of ReDA-PKC, we can formulate AdaPKC (R"; W E ( RC RCa for each TG as follows,
AdaPKC (R1; W) Vec (fEi w/) (xc - X (1)
)T Cue
5=
(SI)
where, w) E RC (j = -.. Cou)..
where, W is the general learning weights of AdaPKCs; Vee(.) is the vectorization operator.
A.2 Multi-view RSS Framework
For multi-view RSS tasks on public radar datasets, in alignment with the design of PKCIn-Net 32] we replace the ReDA-PKC in PKCIn-Net with A or AdaPKC And this brings two multi- view RSS models, denoted as AdaPKCS-Net and AdaPKC -Net correspondingly, whose overall frameworks are depieted in Fig. ST The remaining components of both AdaPKC-Nets keep consistent with PKCIn-Net and TMVA-Net [19] i) the designed networks take RD, RA and AD tensors as inputs and conduct semantic segmentation both RD and RA views concurrently; ii) each encoding branch for a single view comprises two 3D convolution blocks, allowing for the comprehensive utilization of temporal information; iii) an ASPP 2 module used as a component of the encoding branch to aggregate multi- scale spatial information; iv) after the encoding phase, Latent Space Encoder (LSE) aids in integrating the encoding features from multiple views and transmits them to the decoding section for the final prediction.
A.3 Single-view RSS Framework
For single-view RSS tasks on our self-collected radar dataset, we establish a lightweight baseline model called KuRALS-Net, the architecture of which is illustrated in Fig. S2 This model takes temporally contiguous multi-frame RD tensors as inputs, utilizing an Encoder with 3D convolutions to simultaneously gather temporal and spatial information. Then, the encoding features pass through 2D convolution blocks followed by an ASPP module for further information fusion. Finally, the fused features are fed into the Decoder to obtain pixel-wise semantic predictions in the RD view. For the validation of AdaPKC, we replace the 2D convolution blocks in KuRALS-Ner with different AdaPKC blocks.
B Supplementary Details of AdaPKC
B.1 Analysis of Selection Criterion in A
We provide detailed analysis of the selection criterion adopted in Eq. 3 In Eq. 2 the metric score & for the 4h PRF is calculated by averaging the metric scores of related reference units ) For reference units mainly occupied by s' E S.n. the corresponding & is statistically smaller than those primarily influenced by s' E S, and larger than those controlled by s' E S,, When it reflected on the curve of sort(E), & is likely to lie near the quickly descending position. In view of this
LN
13

---

MultiiFramms
AdaPKC Block
ASPP
Encoder
RD Encoding Branch
RD Decoder
RD prediction
Mutri-frames
AD Encoding Branch
AD Imput
LSE
RA Decoder
RA output prodiction
Multi-framrs RA Encoding Branch
RA impai
Mare-Pool
Is 203 Conv
Concaicration
Encoding Flow
Multi View
Latent Space Encoding Floo
Figure $1: The overall frameworks of AdaPKC -Net and AdaPKC -Net, which only differ in AdaPKC Block. Please note that the AdaPKC Block consists of two AdaPKC layers.
ASPP
Multi frames
RD Encoder
Conv Block
RD Decoder
RD prediction
Max Pool
Ixl Comy
Concatenation
Figure S2: The overall framework of KuRALS-Net. The Conv Block can be readily replaced with different AdaPKC blocks to validate the effectiveness of AdaPKC.
attribute, we might adopt the median value E the selection criterion. However, while the use of
median value can locate & where it is desired in some case, it is clear that the maximum value of the first order gradient more robust for variety scenarios, as illustrated in Fig. S3 Therefore, to promote the robustness of this selection, finally employ the maximum value of the first-order gradient.
B.2 Reference Units Sampling and Back-propagation A
The sampling values of reference units in down, left and right directions are obtained by the following forms of linear interpolation, accordingly:
G) x,1
1 - b - [o(ti)]
x, +16 -1,
(S2)
(b 1b9t 1) 1p0 -11 (1,
G)f
[] - (6 - [6_
x,,9 1 ,,,2 6 -11
(S3)
(6 (b X,02 X,02 -
14

---

median value
maximum of the first-order gradient
1.0
sore
0.5
0.0
20
30
50
k
1.0
sorttd 3
0.5
0.0
0
10
20
30
40
50
k
1.0
Corr(a
0.5
S00
0.0
0
10
20
30
40
50
k
Figure S3: The comparison between different selection criteria across three representative scenarios. The median value fails to capture lurget-interfering noise in the first and last scenarios, whereas introduced selection criterion demonstrates robustness in locating it.
X
[1 - (b - [ X 12 + (1 [ x X an +
(S4)
For back-propagation of gradients in these linear interpolations, compute the gradient of Eq. S] w.r.t b [b_1, b Bot bct) as
{s w/ (x. - xi (
Cus
@Vec
1=1
Oboi
C...
(S5)
N
(xx00t
=
w (3)
Ob
Since (x, (0) J determined by the guard bandwidth offset in its corresponding direction, there only remain four non-
(0) Ji=1 have been split into four subsets based on their directions, and each X is solely
zero items in (x N
(x(5))
8x
Git
(x
i.e.
and
where E [1, MJ. For simplicity, 26
abur
1=1"
06
06
a
8xG GM
we demonstrate the calculation procedure with
as an example, and the remaining items follow a
similar approach. From Eq.
S4
we get
fx
X 00 12 +2--165-111
+
(S6)
Gt
With the combination of Eq.
S5 and
S6
obtain the desired gradients.
15

---

C
Visual Illustration of FiTOS
Feature Map
Fixed PRF
AdaPRF
Thresholding On-line Switch
Pretraining Flow
Finetuning Flow
PeakConv
Load weights
PeakConv
Confidence threshold,
Figure S4: The illustration of FiTOS strategy. The Thresholding On-line Switch is detailed in Eq.
The detailed process of FITOS strategy is illustrated in Fig. S4
D Supplementary Details about Datasets and Training Setups
D.1 Detailed Description of CARRADA-RAC Dataset
The CARRADA-RAC [3] dataset is derived from CARRADA and mainly calibrates the original RA annotations. For the generation of both RD and RA annotations. CARRADA adopts a semi- automatic method relying on the Mean- Shift clustering [3]. However, in CARRADA, the clustering performance is seriously degraded by unreliable centroid initialization from optical images and inaccurate candidate search space in RA representation. To alleviate these issues, CARRADA-RAC proposes an RD association strategy to refine the initial centroid, and introduces regionalized CFAR for readjusting the search space.
D.2 Implementation Details
Multi-view RSS. The input sizes of RA, AD and RD views are 256 x 256, 256 x 64 and 256 x 64, respectively. Both AdaPKC-Nets leverage sequence of input frames for temporal information aggregation, consistent with PKCIn-Net 332. We train all these models on two NVIDIA-3099 GPUs and use the Adam optimizer [14 for training. The initial learning rate le 4, and decays in cosine manner by default. We train these models for 300 epochs with a batch size of 6. For FITOS, 300 epochs are evenly distributed between the pre-training and fine-tuning stages, and we set 0.6 for the fine-tuning stage by default. We train these models using a combination of weighted cross-entropy loss, Dice loss and coherence loss, configured with the recommended parameters outlined in [19]
16

---

Single-view RSS. In contrast to the multi-view RSS task, the experimental configurations for KuRALS dataset maintain consistency with the following two exceptions: the input includes only single view of RD tensor after O-Doppler frequency elimination, with shape of 2048 x 124; ii) the
weighted cross-entropy loss in multi-view RSS substituted with weighted focal loss [15] tackle the severer target-backgroum imbalance in KuRALS.
E
More Exploration of AdaPKC mechanism
E.1 Analysis of Guard Bandwidth Distribution in AdaPKCS-Net
We analyze the guard bandwidth distribution in AdaPKC -Net from two perspectives: different categories and different views. The distribution histogram of guard bandwidths in AdaPKC -Nt illustrated in Fig. [S5] From the perspective of different categories, we observe that the guard bandwidth of the background class tends toward uniform distribution, while that of the foreground class exhibits a more distinctly concentrated trend. For a better illustration, we present the variance values of different categories in Tab. [ST] and the remarkable difference in variance between foreground and background classes confirms our observation. This observation aligns with the characteristics of our proposed metric score, indicating that AdaPKC consistently enhances features of foreground- class targets, thus improving their discriminability from the background class. From the perspective of different views, this concentrated trend appears more apparent in the RA view compared to the the guard bandwidth of (2, 1), while
RD view. Concretely, RA view shows clear concentration RD view exhibits considerable proportions on all guard bandwidths except (1, 1). Moreover, this concentrated guard bandwidth in the RA view remains consistent across different AdaPKC layers, while that of the RD view transitions from (2, 3) in the first layer to (1, 2) in the second layer. This comparative analysis of different views indicates that the guard bandwidth selection in RA view demonstrates more confidence and ease for AdaPKC, which explains the better performance improvement of AdaPKC on the RA view.
background
pedestrian
cyclist
car
0.35
0.7
0.6
Probubility
0.25
Probubility
0.5
0.4
0.15
0.3
0.1
0.2
0.05
0.1
0133)
12,
(2,
3)
(1,1) 1)
(1,2)
(1,3)
(2,
(2.2)
(2,
(R, D): bandwidth the 1st layer RD view
(R, A): bandwidth the layer RA view
0.35
0.7
0.3
0.6
Probability
0.25
Probability
0.5
0.2
0.4
0.15
0.3
0.1
0.2
0.05
0.1
(1,
(1,2)
(1.3)
12,
(2,
(2, 3)
(),
(1,2)
(1,3)
(2,
(2.2)
(2.3)
(R, D): bundwidth in the 2nd layer of RD view
(R. bandwidth in the 2nd layer of RA view
Figure S5: The category-wise probability distribution histogram of guard bandwidths in
Aee
17

---

Table S1: Comparison of the variance values for different categories. Varlat and Var represents variance value of the first and second AdaPKC layer, respectively.
RD View
RA View
Category
Var (ar""(%)
Var
Varl
Var (%)
Bkg.
0.19
0.19
0.67
0.33
Ped.
0.58
0.49
2.39
1.47
Cyc.
0.69
0.77
4.38
3.43
Car 0.42
0.72
4.33
3.90
E.2 Comparison between AdaPKC and Dynamic CFAR Detectors
We compare the performance of AdaPKC and dynamic CFAR detectors, and the results are shown in Tab, S2 The comparison reveals several significant limitations of CFAR methods: i) They can only detect foreground targets without the ability categorize them; ii) They show poor target identification performance, struggling with complex target and interference scenarios; ii)) They rely on manual parameter tuning and lack adaptive learning capabilities. Therefore, it is evident that enhancing radar target perception paradigms through deep learning is both necessary and practical, serving as one of the key motivations for PKC and AdaPKC.
Table S2: Performance comparison between AdaPKC and dynamic CFAR detectors on the CAR- RADA dataset. Bkg., Ped., Cyc., Car and Frg. represents Background, Pedestrian, Cyclist, Car and Foreground class, respectively, with Foreground class including Pedestrian, Cyclist and Car classes. FPS is calculated by the total inference time of the detection algorithm on both RA and RD views, using a workstation with an Intel(R) Xeon(R) Platinum 8255C CPU and a Tesla V100-SXM2 GPU.
loU(S))
Dice(%)
View
Methods
FPS
Bkg.
Ped.
Cyc.
Car
Frg.
Bkg.
Ped.
Cyc.
Car
Frg.
CA-CFAR
47.5
82.9
0.9
90.6
1.7
SO-CFAR
36.0
77.8
1.2
87.5
2.4
GO-CFAR
36.6
97.7
2.9
98.8
5.6
RD
OS-CFAR
42.8
93.8
3.1
96.8
6.1
AdaPKCS-NC -Net
20.7
99.7
55.1
31.9
58.2
99.8
71.0
48.2
73.4
AdaPKC -Net
18.8
99.7
54.6
33.7
58.1
99.8
70.6
50.4
73.5
CA-CFAR
47.5
89.2
0.4
94.3
0.8
SO-CFAR
36.0
75.8
0.4
86.2
0.8
GO-CFAR
36.6
97.2
0.6
98.6
1.2
RA
OS-CFAR
42.8
91.6
0.5
95.6
1.0
AdaPKC--Nel
20.7
99.8
24.4
17.1
35.0
99.9
39.3
29.2
51.9
AdaPKC -Net
18.8
99.8
26.5
15.8
32.3
99.9
41.9
27.3
48.9
E.3 Exploration of Adaptive Sampling Strategies for Reference Band
We demonstrate the exploration of adaptive sampling strategies for reference band. Sampling for reference band plays an indispensable role in multiple applications of peak convolutions: for PKC with different guard bandwidth settings, sampling fixed N reference points ensures consistent parameter counts; ii) for AdaPKCs, different x, within the feature map might claim diverse PRFs, necessitating reference points sampling to meet the requirement of convolution operations; iii) for future research on reference bandwidth adjustment, this sampling mechanism operates a manner akin to that in AdaPKCs. For simplicity of exploration, as the subject of our experiment. In addition to the common uniform sampling, we explore two adaptive sampling strategies based on the similar principle of AdaPKC For the candidates of reference points {x(*) _N. in a fixed reference band, we first compute their inner product (similarity) Jim1
take the mentioned application in PKC
with X., ie., X.X,T. Then, the first version (vl) of adaptive sampling selects the N least similar reference points to locate target-interfering noise, while the second version (v2) adaptive sampling chooses the N reference points with intermediate similarity instead. To evaluate the effectiveness
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of these sampling strategies, we conduct comparative experiments on the CARRADA dataset, with the guard bandwidth of PKC fixed at 2 for all dimensions. The evaluation results are shown in Tab. [S3 Compared with uniform sampling, adaptive sampling v1 consistently shows improvement across two views, further affirming the effectiveness of our introduced metric. In the case of adaptive sampling v2, we observe a similar trend as with AdaPKC which exhibits a more pronounced performance enhancement for RA view. Nevertheless, in comparison to the adaptive guard bandwidth adjustment in AdaPKCs, these adaptive samplings of reference band encompass a more limited range of adjustable receptive field, thus demonstrating less improvements on RSS performance.
Table S3: Comparison of different sampling strategies for reference band on the CARRADA dataset. The best and secondary results are marked with bold and underline, correspondingly.
Sampling Strategy
#Params @ Frames
RD View
RA View
mloU
mDice
mIoU
mDice
Uniform sampling
6.3M@5
60.9%
72.9%
42.8%
53.6%
Adaptive sampling
6.3M @
61.3%
73.1%
43.2%
53.7%
Adaptive sampling v2
6.3M
61.0%
72.7%
43.5%
54.1
E.4 Exploration of Enlarging PRF Search Space in AdaPKC
To achieve AdaPRF estimation in a wider range of PRF search space under fixed resource constraints, we propose novel training strategy named Voting-driven Multi-round Training (Vot-MRT). Due to limitations in GPU memory resources, AdaPKC restricts the selection of AdaPRF within local range. To determine the AdaPRF in wider range of PRF search space, Vor-MRT employs a step-wise local optimization strategy. inspired by greedy algorithms
Concretely, Vor-MRT involves multiple rounds of training, and each successive round provides hetter initialization for the guard bandwidth search space, !2 of the latter round. Given that different pixels may pick distinct guard bandwidths, Vor-MR/ implements a voting mechanism to gather the most concentrated guard bandwidth for the subsequent round. The detailed algorithm of Vor-MRT strategy is presented Algo. [
To evaluate the effectiveness of VoI-MRT, we conduct a quantitative experiment on CARRADA dataset. We keep N. consistent with Ω denoted in 8 3.1..1 and the value of N to 3 by default, indicating that the original training process of AdaPKC will be repeated for three rounds in Vor-MRT. The results are shown in Tab. [S4 and it can be observed that Vor-MRT brings visible segmentation performance improvements to AdaPKC -Net by gradually enlarging the search space of alternative PRFs.
F
Supplementary Experiment Results
F1
Semantic Segmentation Results by Category on CARRADA
The category-wise semantic segmentation results on the CARRADA- dataset are shown in Tab. S5 Compared with TMVA-Net, PKCIn-Net employs PeakConv to replace the regular convolution, resulting in impressive performance improvements. Our proposed methods further enhance the capabilitics of PeakConv, leading to significant enhancements across nearly all classes and views, with minor decrease observed in the car class of RA view. Consequently, the proposed methods achieve superior RSS performance and a better trade-off among different classes, which still holds true when compared to other SoTA RSS models.
F.2 Performance vs. Complexity
The trade-off' between performance and complexity of different RSS models is presented in Fig. S6
F33 Qualitative Comparisons on CARRADA
We present in Fig. S7 the qualitative comparisons of different methods on three frames from the CARRADA test split, with each frame exhibiting varying levels of interference with targets. In the
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Algorithm 1: Voting-driven Multi-round Training
Input: Initial guard bandwidth search space: 22 = (162) (g) bg E -2 by E 2
Number of training rounds: N.
Initialization:
The maximum selection frequency of by and by. {C2 0, C. = 0}. for i + to N - do
AdaPKC NNt* + Getting AdaPKC Net trained from scratch with 2
F; + Getting the feature map calculated by AdaPKC
for d in {x, y do
for b9 in D2 dii do
Ca + Getting the cumulative frequency of by being selected across all units F;:
if Ca 2 C then
C² Cd:
end
end Cj = 0;
end
G
(1 b:
22 1+1
f Getting a new local search space extending from b
end
Output: Return AdaPKCS-Net*
Table S4: Results of AdaPKC--Ne with Vot-MRT strategy on CARRADA dataset. The number of training rounds is by default.
RD View
RA View
Round
mloU
mDice
mIoU
mDice
61.2%
73.1%
44.1%
55.1%
2
61.6%
73.6%
43.7%
54.5%
61.8%
73.8%
44.6%
55.7 %
first frame, where the target is interfered with minor noise/clutter, results indicate that all methods can accurately locate and classify targets in relatively clean RD view. However, in the RA view, methods without peak convolution struggle to identify target regions completely in the presence of stronger interference. The interference suppression capability equips PKCIn-Net with superior identification performance, while AdaPKC$-Net FVT further strengthens this capability and recognizes complete target regions. In the second frame, as clutter interference on targets intensifies, PKCIn-Net can still accurately locate targets in both RD and RA views but faces challenges in differentiating target categories in the highly cluttered RA view. Nevertheless, AdaPKC -Ne correctly classifies targets in both views. In the third frame, where the signal of the distant car target is weak and the clutter interference is strong, existing methods miss the car target whereas AdaPKC -eett""-Net"" FVT can still identify it in the RD view. These results suggest that AdaPKC "VT exhibits stronger interference suppression and target recognition capabilities compared to PKCIn-Net and other RSS methods.
F.4 Semantic Segmentation Results by Category on KuRALS
Considering the limited samples of both ship and land vehicle classes, we merge the two classes into single vehicle class to mitigate the negative impact of class-imbalance on model training. This class merging is primarily based on two observations: i) both ships and land vehicles belong to rigid transportation vehicles, whose reflected radar signals share considerable similarities; ii) ships and land vehicles are primarily used for marine monitoring tasks and land detection tasks, respectively, and there exist nearly overlapping application scenarios between them, which minimizes the risk
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Table S5: RSS performance comparison by category on the CARRADA benchmark.
View
Frameworks
AParams @Frames
loU($)
Dice(
Bkg.
Ped.
Cyc.
Car
mloU
Bkg.
Ped.
Cyc.
Car
mDice
FCN
134
99.7
47.7
18.7
52.9
54.7
99.8
24.8
16.5
26.9
66.3
U-Net 1
99.7
51.0
33.4
37.7
67.5
54.7
68.0
DeepLabv3+
59.3M@3
99.7
43.2
11.2
49.2
50.8
99.9
60.3
20.2
66.0
61.6
RSS-Net
10.1M@3
99.3
0.1
4.1
25.0
32.1
99.7
0.2
7.9
40.0
36,9
RAMP-CNN
106.4M@9
99.7
23.2
99,9
37.7
70.8
RD
TMVA-Net
5.6M@5
99.7
49.5
22.8
52.5
56.1
99.8
66.2
37.1
68.9
68.0
TransRadar
4,9M@5
99.6
49.5
24.7
54.8
57.2
99.8
66.2
39.7
70.8
69.1
PKCIn-Net
6.3M@5
99.7
54.0
30.4
58.5
99.8
70.1
46.7
73.9
72.6
AdaPKC. Net(ours)
6
99.7
54.6
33.7
58.1
61.5
99.8
70.6
50.4
73.5
73.6
AdaPKCS- -Net PST (ours)
6
99.7
56.3
32.8
59.6
62.1
99.9
72.0
49.4
74.7
74.0
FCN
134.3M@3 3
99.8
14.8
0.0
23.3
34.5
99.9
25.8
0.0
37.8
40.9
U-Net
17.3M@3
99.8
22.4
8.8
32.8
36.6
16.1
0.0
38.2
DecpLabv3+
59.3M
99.9
3.4
5.9
21.8
32.7
99.9
6.5
11.1
35.7
38.3
RSS-Net
1
99.5
7.3
5.6
15.8
32.1
99.8
13.7
10.5
27.4
37.8
RAMP-CNN
106.4M
99.8
2.6
99.9
13.5
RA
TMVA-Net Net
5.6M
99.8
25.6
7.5
17.8
37.7
99.9
40.7
13.9
30.2
46.2
TransRadar
4
99.7
20.7
11.1
28.2
39.9
99.8
34.3
20.0
44.0
49.5
T-RODNet
162.0M@4
99.9
25.4
9.5
43.5
99.9
40.5
17.4
56.6
PKCIn-Net
6.3M@5
14.8
38.7
25.8
50.4
AdaPKC" -Netiours)
(Neticc-Netiours)
6.3M@5
99.8
26.5
15.8
32.3
41.9
48.9
AdaPKCS-NC -et (ours)
6.3M@5
99.8
26.2
19.2
32.2
48.4
55.5
Performance-VS.Complett (mDice)
RD Performannc-VS--Compleeee (mlot)
55
65
mDkie (%)
mlot (%)
50
40
45
40
35
30
50
100
150
20
50
1.00
120
140
Model (Millions)
Num. Model Purums. (Millions)
Coupplee (mDice)
RA Performane-VS--oommee (mlol.
60
43
55
41
50
miNee (1)
inoot (%)
35
40
35
31
29
27
35
33
100
150
50
100
150
Model (Millions)
Num. Model Params. (Millions)
U-Net
Deeplaabv3+
TMVA-Net
TransRadr
T-RODNet
PKCCin-Net
A (Ours))
Figure S6: Performance vs. Complexity.
21

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(A)
(M)
(0
(4)
(b)
(4)
(0
(c)
(d)
(4)
(
(c)
(d)
(6)
Figure S7: Qualitative comparison of different methods CARRADA. The three frames from CARRADA test split are with varying levels of interference on targets. For each frame, the top row shows the camera image and the results in RD view. while the bottom row shows the results in RA view. (a) RD/RA tensor, (b) the label of mask, (c) TMVA-Net, (d) TransRadar, (c) PKCIn-Net and (f) AdaPKC PIT (ours). Different colors represent different object categories. Black: background, Red: pedestrian, Yellow: eyclist, Cyan: car.
22

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of practical application issues due to recognition confusion. As result, we perform the segmentation task with four categories: background, UAV. pedestrian and vehicle. The semantic segmentation results by category on the KuRALS-Tess, dataset are presented in Tab. S6
Table S6: RSS performance comparison by category on the KuRALS Test dataset.
Frameworks
*Prrams
(o6)
Dice(%)
UAV
Ped.
Veh.
mloU
Bkg
UAV
Ped.
Veh.
mDiie
PCN
134.3M@5
99.9
68.1
21.9
11.5
50.4
99.9
81.0
35.9
20.6
59.4
17.3M@
99.9
DaepLabb3+
59,3M@5
99.9
73.1
23.0
14.4
52.6
99.9
84.5
37.4
25.2
61.8
1.2@55
99.9
72.8
39.0
12.2
56.0
99.9
56.1
65.5
KuRALS-Net (KClours-uuselie)
1.2@85
79.7
56.7
48.4
26.5
65.9
KuRALS Net w/ AdaPKC" (ours)
1.2M@5
99.9
78.4
33.2
19.4
57.8
99.9
87.9
49.9
32.6
67.6
KuRALS-Net w/ AdaPKC (ours)
1.2@@5
99.9
81.5
3166
19.6
58.2
99.9
89.8
48.0
32.7
67.6
F,5 Qualitative Comparison on KuRALS
The qualitative results on KuRALS dataset are illustrated in Fig. methods and other segmentation models, our AdaPKCs demonstrate more accurate localization and classification targets.
S8
Compared to our baseline
Figure S8: Qualitative comparison on KuRALS. (a) RD tensor, (b) the label of mask, (c) FCN, (d)
U-Net, (e) DeepLabv3+, (f) KuRALS-Net bours-baseline) (g) KuRALS-Ner w/ PKC basel-bas (h) KuRALS-Net w/ A FXT (ours). Different colors represent different object categories. Black: background, Red: UAV, Yellow: Pedestrian, Cyan: Vehicle.
F.6 More Results in Ablation Study for FITOS
Fig. S9
illustrates the segmentation results of mloU for AdaPKC$-Net""7 with different values of
T
1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}.
AdaPKCS-My Fir
AdaPKC--Net
PKCIn-Net
mlot
mloU
44.0%
61.0%
60.5%
43,0%
0.2
0.4
0.5
0.6
0.7
0.9
View
RA View
Figure 89: RSS performance of mloU for Nee with different values of 5
10.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7.0.8.0.)..
23

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G Broader Discussions
Limitations. We have collected a Ku-band continuous wave radar dataset to validate the effectiveness of our proposed method in surveillance radar detection scenarios. Nevertheless, pulse-Doppler radar also commonly used in these scenarios and investigation on such radar datasets would further uncover the potentials and issues of our methods in practical applications. However, there is currently a lack of publicly available datasets for pulse- Daopper monitoring radar. To alleviate this issue, we will try to collect and release Doose-- radar dataset to enable more comprehensive validation and analysis our methods and other works.
Societal Impacts. Our approach is applicable to various practical applications, such as perceptions for autonomous driving, UAV surveillance and marine monitoring, However, inappropriate usage may lead to decreased reliability, potentially resulting in safety and other issues.
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Reviews:
[{"review_id": "gU45BrdeHL", "reviewer": "Reviewer_ixjc", "summary": "This paper works on the improvement of Radar semantic segmentation. Motivated by the limitation of learning ability of the SoTA PKC method due to the fixed peak receptive field (PRF), an adaptive version named AdaPKC is proposed.  The method can be metric-based and learning-based.  The advantages of the proposed method are validated by the extensive experiments  on two datasets.", "questions": "- How are the frame rates for $AdaPKC^θ$, $AdaPKC^ξ$ and PKC?\n- Is any relationship between the guard bandwidth and the specifics of the Radar sensor?", "limitations": "The limitation of the evaluation of the method for pulse-Doppler radar is discussed.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The motivation of the study is practical, and the paper is well-written and easy to follow. The figures are well-illustrated .\n- The experiments, visualization and analysis are extensive.\n- The proposed AdaPKCs outperform the state-of-the-art baselines.", "weaknesses": "- The novelty of the proposed method is limited, considering the existing PKC work.\n- Although the method is practical and lightweight, the performance gain is not significant.\n- I also have concerns about the practicality of the problem setting for radar semantic segmentation in this work. It appears to be more similar to the object detection task found in other datasets. Additionally, other modalities such as cameras or LiDAR are usually available and can provide complementary information even in adverse weather conditions. Moreover, scanning Radar and 4D Radar sensors (such as ORR, Radiate, and K-Radar datasets), which offer much higher resolution, are also becoming increasingly popular.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments on our work. We appreciate the recognition of the strengths of our work: the practical motivation and good presentation, extensive experiments and analysis, and superior performance than SoTA. We are glad to answer all your questions.\n\n**Q1:** The novelty of the proposed method is limited, considering the existing PKC work.\n\n**A1:** Please see our response to all authors that clarify the **[Novelty]** of our work.\n\n**Q2**: Although the method is practical and lightweight, the performance gain is not significant.\n\n**A2**: We are sorry that the illustration of performance comparison may be not clear enough that cause some confusion. However, the performance improvement of this work is not small for the Radar Semantic Segmentation (RSS) task.\n\n- **From the perspective of performance improvements in previous state-of-the-art methods**, **these performance improvements are rather hard and slow**. Unlike optical images, radar frequency maps lack shape information for the targets and contain significant interference, making the RSS task particularly challenging. TMVA-Net shows improvements over RAMP-CNN only in the RA view, while exhibiting decreased performance in the RD view; TransRadar introduces numerous Transformer and convolution modules, yet offers only an average improvement of 1.1% over TMVA-Net in the RD view; and PKCIn-Net demonstrates almost no improvement over T-RODNet in the RA view. In summary, from the initial FCN model to our AdaPKC-Net, the mDice in RD view has advanced only from 66.3% to 74.0%. In contrast, our models, with modifications only to fundamental convolution kernels, achieve an average improvement of 1.4% over SoTA methods in the RD view of the CARRADA dataset and an average improvement of 1.5% in the RA view, which is a notably significant enhancement.\n- **From the perspective of comparison with DCNv2**, ***In Table R2***, we compare the performance improvements of AdaPKC and DCNv2 over their respective baseline convolution operators, and results show that even when PKC has already reached much higher performance than DCNv1 on CARRADA, AdaPKC still achieves larger performance increase compared to DCNv2, highlighting the effectiveness of our proposed approach.\n- **From the perspective of comparison with PKC**, ***in Table R3***, we present the performance improvements of AdaPKC on the Ku band radar dataset. It can be observed that even when PKC fails to deliver satisfactory results, AdaPKC is still able to improve the situation and achieve more significant performance enhancements over PKC.\n\n**Q3**: The practicality of the problem setting for radar semantic segmentation (RSS) in this work.\n\n**A3**: We clarify each of the concerns you have raised:\n\n- ***“It appears to be more similar to the object detection task found in other datasets”**.* In datasets like CRUW, radar object detection (ROD) tasks use a single point on the radar frequency map as target labels. However, in practical scenarios, multiple positions of a target reflect echoes, and the Fast Fourier Transform process in generating radar frequency maps can cause unavoidable spectral expansion. This results in more than one pixel belonging to the target range, thus mask labels in RSS task can better cover the target range. Furthermore, during training for ROD tasks, researchers (i.e., RODNet, T-RODNet) typically expand the single-point labels using methods like Gaussian smoothing to better cover the target area, whereas RSS labels inherently match the target distribution without requiring such preprocessing.\n- ***“Other modalities such as cameras or LiDAR are usually available and can provide complementary information even in adverse weather conditions”.*** This work focuses on developing general perception methods for different radar systems and in various scenarios, while modalities such as cameras and LiDAR may fail to provide effective information in certain scenarios. For instance, the Ku-band radar used in our work for UAV surveillance and marine monitoring tasks has a maximum detection range of 6375 meters, far exceeding the effective detection range of cameras and LiDAR.\n- ***“Scanning Radar and 4D Radar sensors (such as ORR, Radiate, and K-Radar datasets), which offer much higher resolution, are also becoming increasingly popular”.*** High-resolution radars are more expensive and generate much larger data volumes within the same detection range, making them unaffordable for the aforementioned long-range detection scenarios. Additionally, datasets including ORR, Radiate, and K-Radar only provide bounding box labels, which offer a lower level of granularity in depicting target areas compared to the mask labels used in RSS tasks.\n\n**Q4**: Frame rates for AdaPKC and PKC.\n\n**A4**: Please see our response to all authors that illustrates the **[Complexity and FPS]** of our work and PKC.\n\n**Q5**: Relationship between the guard bandwidth and the specifics of the Radar sensor.\n\n**A5**: The guard band is indeed influenced by the specific characteristics of the radar sensor. Setting the guard band ensures that the energy at the center point does not leak into the interference estimation process. Appropriate guard band settings are affected by the i) electromagnetic scattering characteristics of the target, including effective radar cross section (RCS), the appearance material , motion characteristics; ii) the detection environment of radar, such as environmental clutters and weather, which would cause different interference distributions; iii) radar's own operating mode, frequency band, waveform modulation and transmit power, thus resulting in different range and Doppler resolution. Finally, various factors will affect the gurad bandwidth setting. Hence, motivated by the impact of various factors on guard bandwidth, we try to design an adjustment mechanism that could be both automatically learned (absent in CFAR) and data-driven (missing in PKC), and that leads to AdaPKC.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EeZD6DpXTm", "reviewer": "Reviewer_qbsg", "summary": "This paper proposes an idea of adaptive peak receptive field, and upgrades PKC to AdaPKC based on this idea. Beyond that, a novel fine-tuning technology to further boost the performance of AdaPKC-based RSS networks is presented.", "questions": "It is not clear how many annotated radar data is sufficient to train the proposed network. \nWhat is complexity of the proposed network? Is it running faster than CFAR with adaptive reference cell selection?", "limitations": "Limitation of the proposal is not discussed in the submission.", "rating": 3, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The adaptive version of PeakConv (PKC) is motivated by the adaptive selection of reference cells in the classical radar detector, CFAR.", "weaknesses": "The adaptive version of PeakConv (PKC) is considered to be incremental work. The numerical results also indicated the AdaPKC performance improvement over PKC is very limited. \n\nThe comparison with classical CFAR with adaptive reference cell selection is not included in the validation part.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments. We are glad to answer all your questions.\n\n**Q1:** The adaptive version of PeakConv (PKC) is considered to be incremental work.\n\n**A1:** Please see our response to all authors that clarify the **[Novelty]** of our work.\n\n**Q2:** AdaPKC performance improvement over PKC is very limited.\n\n**A2:** We are sorry that the illustration of performance comparison may be not clear enough that cause some confusion. However, the performance improvement of this work is not small for the Radar Semantic Segmentation (RSS) task.\n\n- **From the perspective of performance improvements in previous state-of-the-art methods**, **these performance improvements are rather hard and slow**. Unlike optical images, radar frequency maps lack shape information for the targets and contain significant interference, making the RSS task particularly challenging. TMVA-Net shows improvements over RAMP-CNN only in the RA view, while exhibiting decreased performance in the RD view; TransRadar introduces numerous Transformer and convolution modules, yet offers only an average improvement of 1.1% over TMVA-Net in the RD view; and PKCIn-Net demonstrates almost no improvement over T-RODNet in the RA view. In summary, from the initial FCN model to our AdaPKC-Net, the mDice in RD view has advanced only from 66.3% to 74.0%. In contrast, our models, with modifications only to fundamental convolution kernels, achieve an average improvement of 1.4% over SoTA methods in the RD view of the CARRADA dataset and an average improvement of 1.5% in the RA view, which is a notably significant enhancement.\n- **From the perspective of comparison with DCNv2,** we compare the performance improvements of AdaPKC and DCNv2 over their respective baseline convolution operators ***In Table R2***, and results show that even when PKC has already reached much higher performance than DCNv1 on CARRADA dataset, AdaPKC still achieves a larger performance increase compared to DCNv2, highlighting the effectiveness of our proposed approach.\n- **From the perspective of comparison with PKC,** we present the performance improvements of AdaPKC on the Ku band radar dataset  ***in Table R3***. It can be observed that even when PKC fails to deliver satisfactory results, AdaPKC is still able to improve the situation and achieve more significant performance enhancements over PKC.\n\n**Q3:** The comparison with classical CFAR with adaptive reference cell selection is not included in the validation part.\n\n**A3:** Thank you for your constructive feedback. We have added a comparison with various CFAR methods ***in Table R1*** of the Rebuttal PDF, and the comparison reveals several limitations of CFAR methods: (1) they can only detect foreground targets but cannot distinguish specific categories of these targets; (2) they show poor target identification performance, struggling with complex target and interference scenarios; (3) they rely on manual parameter tuning and lack adaptive learning capabilities. Therefore, it is both necessary and practical to improve radar target perception paradigms using deep learning methods (it is also one of the motivations for both PKC and AdaPKC).\n\n**Q4:** How many annotated radar data is sufficient to train the proposed network？\n\n**A4:** We use the same amount of annotated data as in previous works such as PKC. For the CARRADA dataset, the training set includes a total of 8088 labeled frames. For the KuRALS dataset, the training set comprises 2064 labeled frames.\n\n**Q5:** Complexity of the proposed network.\n\n**A5:** We compute the computational complexity and frame rates of AdaPKC and PKC on a Tesla V100 GPU and summarize it as follows. Compared to PKC, AdaPKC incurs minimal additional computational complexity and inference speed overhead.\n\n| Dataset  | Conv Type | GMACs |   Runtime (ms) |    FPS  |\n|---| ----------- | ----------- | :---: | --- |\n| CARRADA | PKC      | 109.8   | 47.5 | 21.1 |\n| CARRADA | $\\text{AdaPKC}^{\\xi}$ | 109.8 | 48.4 | 20.7 |\n| CARRADA | $\\text{AdaPKC}^{\\theta}$ | 110.1 | 53.2 | 18.8 |\n| KuRALS | PKC      | 162.4   | 49.4 | 20.2 |\n| KuRALS | $\\text{AdaPKC}^{\\xi}$ | 162.4 | 50.6 | 19.8 |\n| KuRALS | $\\text{AdaPKC}^{\\theta}$ | 162.6 | 52.0 | 19.3 |\n\n**Q6:** Is it running faster than CFAR with adaptive reference cell selection?\n\n**A6: *In Table R1*** of the Rebuttal PDF, we compare the fps of AdaPKC with CFAR methods. Currently, AdaPKC's inference speed is lower than that of CFAR methods, constrained by the original inference speed of PKC. However, the detection performance of CFAR methods is much worse than AdaPKC. Furthermore, we intend to leverage CUDA acceleration to improve AdaPKC's inference speed going forward.\n\n**Q7:** Limitation of the proposal is not discussed in the submission.\n\n**A7:** Sorry for the confusion! Due to the page limit, we have detailed the limitations in Section G of the Appendix.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DcaYpcCkNq", "reviewer": "Reviewer_mTDV", "summary": "This paper presents a radar semantic segmentation method, AdaPKC, which combines PeakConv and Adaptive Peak Receptive Field (APRF) concepts. The author demonstrates extensive applicability of AdaPKC in radar perception including autonomous driving, drone surveillance, and ocean monitoring. The method significantly enhances radar semantic segmentation performance with robustness and scalability.", "questions": "1.Can you provide a more detailed analysis of the computational complexity?\n2.Can you provide a more detailed analysis of the impact of the proposed AdaPKC on real-time performance?", "limitations": "NA", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The method's performance and efficiency have been demonstrated through experiments to exceed both a strong baseline in radar semantic segmentation.\n2.The author successfully achieved incremental optimization in PeakConv [1], reaching state-of-the-art performance.\n3.Rigorous ablation studies were conducted, providing solid evidence of the proposed method's efficacy.\n4.The paper includes a good review of existing work and contributes to the development of radar semantic segmentation.", "weaknesses": "This method builds upon existing methods and is an improved version of the existing PeakConv (PKC). It combines them in a novel way. The novelty is limited for NeurIPS.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments. We appreciate the recognition of the strengths of our work: superior performance than SoTA, solid evidence of the method's efficacy by rigorous ablation studies, contribution to the development of radar semantic segmentation. We are glad to answer all your questions.\n\n**Q1**: This method improves PKC in a novel way. The novelty is limited for NeurIPS.\n\n**A1**: We apologize for not fully summarizing the novelty of our work. Next, we provide a detailed explanation of the innovative aspects of our research.\n- Conceptually and in principle, to the best of our knowledge, adaptive peak receptive field (AdaPRF) in this work is **the first attempt** specifically tailored for radar signal processing to dynamically adjust the receptive field (RF) for convolution operators, which is a further great breakthrough compared with PKC. Its design is based on both (1) radar signal principles, including radar signal generating mechanism, the difference between target and interference distribution, and (2) deep learning principles, such as differentiable high-dimensional representation learning for data-driven end-to-end network optimization. With AdaPRF the original PKC is updated into a more data-adapted version, AdaPKC, and is verified in different convolution frameworks, which can also be **extended to more radar-oriented learning algorithms and  wider range of radar detection scenarios**. Since **more adaptive interference estimation is always a key topic** in radar signal processing, **AdaPKC is novel and significant.**\n- Additionally, **although PKC has been proposed, its inherent limitations cannot be ignored.** Continuous introduction and validation of new ideas and methods are essential for developing new radar sensing algorithms to better meet practical needs, just like YOLO series.\n- From a design perspective, **AdaPRF is  innovative compared to existing methods**. In this paper, we first analyze existing mature works in both RSS and deep learning fields, then deeply examining their shortcomings from radar perspective, such as PKC's fixed RF issue, the difficulty of making CFAR's dynamic RF learnable, and the limitations of DCN's dynamic RF mechanism in handling radar signals. Finally, rather than simply using or combining existing research, we propose the AdaPKC, and validate it with various real-measured radar data, providing new insights for this research area.\n- Technically, it is worth noting that **implementing AdaPRF is a non-trival task**. We have summarized and explained main challenges and solutions in our paper and code, offering valuable experience for future research, such as:\n    - How to reliably assess the reference points belonging to appropriate PRF in high-dimensional radar signal representation;\n    - How to design the assessment method to meet the requirements of smooth differentiability and parallel computation of deep learning;\n    - How to handle the changing number of sampling points for fixed convolution kernels due to dynamically changing receptive fields;\n    - How to optimize inference to meet the requirements of practical radar signal processing application.\n- Finally, we emphasize that the goal of this work is to meet the practical needs for radar application and contribute effective and efficient component for the next generation of radar signal processing paradigm. We are well aware of the many bottlenecks in exsiting radar signal processing, which has been stagnant for many years, and the challenges in applying deep learning to radar. Therefore, our goal is to provide a practical extension for new radar signal processing methods, freeing them from existing rigid workflows, which is of significant industrial value.\n\n**For the conference of NeurIPS**, our work presents a practical study of adapting deep learning to radar recognition application, aligning with the Call for Papers section of NeurIPS that focus on Applications and Deep Learning, etc, which is also strongly represented in NeurIPS every year.\n\n**Q2**: A more detailed analysis of the computational complexity and real-time performance.\n\n**A2**: We compute the computational complexity and frame rates of AdaPKC and PKC on a Tesla V100 GPU and summarize it as follows. Compared to PKC, AdaPKC incurs minimal additional computational complexity and inference speed overhead.\n\n| Dataset  | Conv Type | GMACs |   Runtime (ms) |    FPS  |\n|---| ----------- | ----------- | :---: | --- |\n| CARRADA | PKC      | 109.8   | 47.5 | 21.1 |\n| CARRADA | $\\text{AdaPKC}^{\\xi}$ | 109.8 | 48.4 | 20.7 |\n| CARRADA | $\\text{AdaPKC}^{\\theta}$ | 110.1 | 53.2 | 18.8 |\n| KuRALS | PKC      | 162.4   | 49.4 | 20.2 |\n| KuRALS | $\\text{AdaPKC}^{\\xi}$ | 162.4 | 50.6 | 19.8 |\n| KuRALS | $\\text{AdaPKC}^{\\theta}$ | 162.6 | 52.0 | 19.3 |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HcLjnG6XII", "reviewer": "Reviewer_i1nF", "summary": "The PeakConv (PKC) model specialized for radar signal analysis effectively characterizes the target signatures of radar signals. However, the fixed predefined peak receptive field limits the performance of PKC due to significant variations in target features and associated interference within radar signals. To solve this problem, the authors propose AdaPKC, aimed at adaptively adjusting the peak receptive fields in PKC through two data-adaptive band-pass filtering mechanisms. The experimental results show that the proposed method is advanced to some extent.", "questions": "1.Please provide a clearer explanation of the content presented in Figure 1. Which part of the Figure 1(a) does the red ellipse in the first row of radar frequency map represent? Why does it disappear in the second radar frequency map of the first row? Why does the purple ellipse reappear in the second row? What do the corresponding image frames look like, and how are they related to these radar frequency maps? Please also supplement the captions for Figures 2 and 3 to facilitate reader comprehension.\n2.In Section 3.1.1, \"...we divide the observed radar signals into three subsets...\". How are these subsets divided? How are the expressions for each segment in Equation 1 derived?\n3.Please add an analysis of the computational efficiency of the model. How much computational cost is added by the two modules proposed?\n4.Dilated convolution can also adaptively adjust the receptive field of the convolution kernel. What are the advantages of your method compared to dilated convolution techniques?\n5. Would the method proposed in this paper still work if other baselines similar to PKC was used?", "limitations": "See Weakness and Questions.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Originality: This paper introduces an adaptive method for adjusting the PRF based on an existing baseline, demonstrating some innovation.\nQuality: The paper is technically sound. The proposed dataset and the results of the proposed model are analyzed in detail.\nClarity: Overall, the paper is clearly written, although some sections require clearer articulation.\nSignificance: The general applicability of the proposed method needs further validation.", "weaknesses": "1.The explanation of Figure 1 is unclear, and the captions for Figures 2 and 3 do not sufficiently summarize the contents depicted, resulting in poor readability.\n2.The experiments lack an evaluation of the model's computational efficiency\n3.The effectiveness of the proposed method requires further validation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive and thoughtful comments. We appreciate the recognition of the strengths of our work: the innovation of our method, technically sound paper and overall clarity in writing. We are glad to answer all your questions.\n\n**Q1**: The correspondence between interference in radar frequency maps and that in image frames presented in Figure 1.\n\n**A1**: \nThank you for your constructive feedback. However, the fluctuating interference in the radar frequency map is indeed difficult to correlate with the optical images, primarily due to the following reasons:\n\n1. **Dissimilar Coordinate Systems and Fields of View**: The radar frequency maps and the optical images have different coordinate system definitions and fields of view, making it challenging to establish a direct correspondence between them. For instance, in Range-Doppler (RD) maps, targets which are spatially distinguishable in the optical image but share the same radial distance will be compressed into the same range bin in the RD map, thus probably becoming indistinguishable. Furthermore, Doppler domain reflects the velocity of target, it cannot be mapped into the optical image.\n2. **Different Imaging Mechanisms**: The radar frequency maps and optical images are formed through distinct mechanisms. The amplitude in a radar frequency map actually represents the strength of the received echoes, which can be affected by invisible interference noise in the optical spectrum and system noise within the radar system itself. These factors do not directly translate into optical images, making it difficult to visually correlate the fluctuating interference seen in the radar frequency maps with the corresponding optical images.\n\nTherefore, while we can observe fluctuating interference in the radar frequency map, it is challenging to visualize this interference using the corresponding optical images.\n\n**Q2**: A clearer caption for Figure 2 and 3.\n\n**A2**: We will add the following descriptions into the captions of corresponding figures.\n\n1. *Caption for Figure 2:* The illustration of AdaPRF in AdaPKC$^\\xi$. (a) illustrates the definition of PRF in PKC, whose area is governed by the reference bandwidth and guard bandwidth; (b) describes the estimation process of AdaPRF in AdaPKC$^\\xi$, including denoting K candidate PRFs for each CUT, translating these PRFs into metric scores, and finally selecting an appropriate PRF as the AdaPRF with these metric scores.\n2. *Caption for Figure 3:*  The illustration of AdaPRF in AdaPKC$^\\theta$. (a) illustrates an example of candidate PRFs in AdaPKC$^\\theta$, where the guard bandwidth is in quadruple form; (b) describes the flowchart of the optimal guard bandwidth estimation network, which consists of two parallel branches that sample representative points in their corresponding directions, then automatically measures and selects the optimal guard bandwidth.\n\n**Q3**: Analysis of the computational efficiency of the model.\n\n**A3**: Please see our comments to all authors that clarify the **[Complexity and FPS]** of our work and PKC.\n\n**Q4**:  The effectiveness of the proposed method requires further validation.\n\n**A4**: The effectiveness of our proposed method has been widely validated within multi-view and single-view radar semantic segmentation frameworks and across three large scale radar datasets. And we are also collecting new real-measured radar datasets to more extensively validate the effectiveness of our methods and other works.\n\n**Q5**: More clearer explanation of the division of observed radar signals and derivation of Eq. (1).\n\n**A5**:  It is natural to devide the radar signals into the three subsets: (1) signals reflected from a target,  $S_t$; (2) noise with signals that leaks out of the target, $S_{t-n}$; and (3) noise, $S_n$. For simplicity, we introduce an attenuation factor $\\eta<1$ to indicate the leaking of signals from targets. For some CUT $x_c=\\psi(s;W)$ and its candidate reference unit  $x_r=\\psi(s';W)$,  \n\n- if $s'\\in S_t$, then $s'$ and $s$ belong to the same target, so $s’=s$ and $E(x_c x_r^T)=E(\\psi(s;W)\\cdot \\psi(s';W)^T)=E(||\\psi(s;W)||_2^2)$\n- if $s'\\in S_{t-n}$, then $s’=\\eta\\cdot s$ and $E(x_c x_r^T)=\\eta E(||\\psi(s;W)||_2^2)$\n- if $s'\\in S_{n}$, then $E(x_c x_r^T)=E(\\psi(s;W))\\cdot E(\\psi(s';W)^T)=0$\n\nFrom this equation, we can see that the inner product transformation assigns three statistical boundaries to $x_r$ from the three subsets, and this attribute significantly serves to facilitate the subsequent localization of reference units from $S_{t-n}$.\n\n**Q6**:  Advantages of AdaPKC compared to dilated convolution techniques.\n\n**A6**: Compared to dilated convolution, AdaPKC has the following advantages: (1) The receptive field of dilated convolution can only be manually adjusted by setting different dilation rates, whereas AdaPKC can adaptively adjust the receptive field for each CUT based on the distribution of the CUT and its surrounding area; (2) While dilated convolution indirectly establishes a guard field around the CUT, it always couples the CUT with its surrounding points during interference estimation and band-pass filtering. In contrast, AdaPKC decouple the CUT from the surrounding points, explicitly estimate interference noise, and perform adaptive band-pass filtering for each CUT.\n\n**Q7**:  Would the method proposed in this paper still work if other baselines similar to PKC was used?\n\n**A7**: Yes! Our AdaPKC is indeed a universal convolution operator. If you review our code, you can find that AdaPKC can be used conveniently ****just like any other convolution operator, and it can be seamlessly integrated into any convolutional framework designed for radar signal processing.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We extend our gratitude to the reviewers for their valuable feedback. In this section, we commence by tackling the concerns that have been collectively raised. These shared concerns correspond to the three keywords in the title:

**[Novelty] What is the novelty of this work compared to existing PKC and other works?**

- Conceptually and in principle, to the best of our knowledge, adaptive peak receptive field (AdaPRF) in this work is **the first attempt** specifically tailored for radar signal processing to dynamically adjust the receptive field (RF) for convolution operators, which is a further great breakthrough compared with PKC. Its design is based on both (1) radar signal principles, including radar signal generating mechanism, the difference between target and interference distribution, and (2) deep learning principles, such as differentiable high-dimensional representation learning for data-driven end-to-end network optimization. With AdaPRF the original PKC is updated into a more data-adapted version, AdaPKC, and is verified in different convolution frameworks, which can also be **extended to more radar-oriented learning algorithms and  wider range of radar detection scenarios**. **Since more adaptive interference estimation is always a key topic** in radar signal processing, **AdaPKC is novel and significant.**
- Additionally, **although PKC has been proposed, its inherent limitations cannot be ignored.** Continuous introduction and validation of new ideas and methods are essential for developing new radar sensing algorithms to better meet practical needs, just like YOLO series.
- From a design perspective, **AdaPRF is  innovative compared to existing methods**. In this paper, we first analyze existing mature works in both RSS and deep learning fields, then deeply examining their shortcomings from radar perspective, such as PKC's fixed RF issue, the difficulty of making CFAR's dynamic RF learnable, and the limitations of DCN's dynamic RF mechanism in handling radar signals. Finally, rather than simply using or combining existing research, we propose the AdaPKC, and validate it with various real-measured radar data, providing new insights for this research area.
- Technically, it is worth noting that **implementing AdaPRF is a non-trival task**. We have summarized and explained main challenges and solutions in our paper and code, offering valuable experience for future research, such as:
    - How to reliably assess the reference points belonging to appropriate PRF in high-dimensional radar signal representation;
    - How to design the assessment method to meet the requirements of smooth differentiability and parallel computation of deep learning;
    - How to handle the changing number of sampling points for fixed convolution kernels due to dynamically changing receptive fields;
    - How to optimize inference to meet the requirements of practical radar signal processing application.
- Finally, we emphasize that the goal of this work is to meet the practical needs for radar application and contribute effective and efficient component for the next generation of radar signal processing paradigm. We are well aware of the many bottlenecks in exsiting radar signal processing, which has been stagnant for many years, and the challenges in applying deep learning to radar. Therefore, our goal is to provide a practical extension for new radar signal processing methods, freeing them from existing rigid workflows, which is of significant industrial value.

**[Performance Improvement] The performance gain is not significant.**

We are sorry that the illustration of performance comparison may be not clear enough that cause some confusion. However, the performance improvement of this work is not small for the Radar Semantic Segmentation (RSS) task.

- **From the perspective of performance improvements in previous state-of-the-art methods**, **these performance improvements are rather hard and slow**. Unlike optical images, radar frequency maps lack shape information for the targets and contain significant interference, making the RSS task particularly challenging. TMVA-Net shows improvements over RAMP-CNN only in the RA view, while exhibiting decreased performance in the RD view; TransRadar introduces numerous Transformer and convolution modules, yet offers only an average improvement of 1.1% over TMVA-Net in the RD view; and PKCIn-Net demonstrates almost no improvement over T-RODNet in the RA view. In summary, from the initial FCN model to our AdaPKC-Net, the mDice in RD view has advanced only from 66.3% to 74.0%. In contrast, our models, with modifications only to fundamental convolution kernels, achieve an average improvement of 1.4% over SoTA methods in the RD view of the CARRADA dataset and an average improvement of 1.5% in the RA view, which is a notably significant enhancement.
- **From the perspective of comparison with DCNv2**, ***In Table R2***, we compare the performance improvements of AdaPKC and DCNv2 over their respective baseline convolution operators, and results show that even when PKC has already reached much higher performance than DCNv1 on CARRADA dataset, AdaPKC still achieves a larger performance increase compared to DCNv2, highlighting the effectiveness of our proposed approach.
- **From the perspective of comparison with PKC**, ***in Table R3***, we present the performance improvements of AdaPKC on the Ku band radar dataset. It can be observed that even when PKC fails to deliver satisfactory results, AdaPKC is still able to improve the situation and achieve more significant performance enhancements over PKC.

**[Complexity and FPS] The computation complexity and frame rates of AdaPKC and PKC.**

***In Table R4 and R5*** of the rebuttal PDF, we summarize the computational complexity and frame rates of AdaPKC and PKC. Compared to PKC, AdaPKC incurs minimal additional computational complexity and inference speed overhead.

Meta-review: This paper received mixed reviews: one Weak Accept, two Borderline Accepts, and one Reject. Reviewers who leaned toward acceptance appreciated the approach but raised concerns regarding the clarity of the technical novelty and the significance of performance improvements compared to baseline results.

In the rebuttal and follow-up discussion, the authors effectively clarified the technical contributions and justified the performance gains, addressing nearly all the reviewers' concerns. As a result, three reviewers agreed on accepting the paper.

The reviewer 'qbsg' (who gave a Reject) provided a brief and insufficiently detailed review. Although the AC requested additional feedback to justify the rating, the reviewer did not respond. Given that the rebuttal adequately addressed all of the reviewer’s initial concerns and no further comments were provided, the AC recommends disregarding this rating.

Based on reviewer comments and authors rebuttal and feedback, the AC recommends paper acceptance. 
Authors are requested to incorporate the reviewers' comments in the final version.
Decision: Accept (poster)