bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust sequential high‐resolution range profile recognition based on the infinite conditional restricted Boltzmann machine
Authors: Gao Xunzhang , Peng Xuan
Published: October 2019

Feature extraction for radar high‐resolution range profiles (HRRPs) is an essential procedure for robust target recognition based on radar HRRPs. It is almost always better to utilise multiple HRRPs from the target than a single HRRP. The commonly used recognition techniques are not robust enough to extract features from HRRP sequences, especially when there is not enough training data. In this study, the authors present a compact model named the infinite conditional restricted Boltzmann machine (iCRBM) for jointly feature learning and recognition of radar HRRPs. The model is developed from the conditional restricted Boltzmann machine, which is a powerful model for representing high‐dimensional and multi‐modal conditional distributions. Unlike ordinary RBMs, iCRBM can automatically select the model complexity according to the data, which especially suits for the case of large data dimensionality and small training dataset. A multi‐scale structure is also proposed for learning multiple scales of temporal/spatial dependencies. Two types of radar HRRP recognition experiments were conducted to validate the efficiency and robustness of iCRBM: the multi‐view HRRP recognition and the small aperture HRRP recognition. The results indicate that, the proposed model can be more adaptive at learning the feature of HRRPs than other common methods. The multi‐scale structure is helpful for better recovering missing HRRP samples from non‐consecutive sequences.
Robust adaptive beamforming for MIMO radar in the presence of covariance matrix estimation error and desired signal steering vector mismatch
Authors: Huang Junsheng , Su Hongtao , Yang Yang
Published: October 2019

Owing to covariance matrix estimation error, desired signal steering vector mismatch, and the existence of target signal in training samples, most of adaptive beamforming algorithms suffer from a great performance degradation. Aim at this, this study proposes a robust adaptive beamforming algorithm for multiple‐input–multiple‐output radar. To be specific, the sample covariance matrix is replaced by the reconstructed covariance matrix, to narrow the difference between the maximum and minimum noise eigenvalues. In addition, the diagonal loading technique is applied to correct the mismatched desired signal steering vector by maximising the output signal‐to‐interference‐plus‐noise ratio of adaptive beamformer, and a simple closed‐form solution to loading factor can be further obtained. Computer simulation results demonstrate that the proposed algorithm can efficiently improve the robustness of adaptive beamformer against both covariance matrix estimation error and desired signal steering vector mismatch.
Advanced square‐root cubature Kalman filters based on singular value decomposition and sequential processing
Authors: Li Zhifei , Zhang Jianyun , Wang Jiegui , Zhou Qingsong
Published: October 2019

Numerical accuracy of the cubature Kalman filter (CKF) is crucially degraded by the accumulated round‐off errors. As a systematic solution to reduce the influence of round‐off errors, the square‐root filters are appealing. This study proposes a square‐root CKF based on the singular value decomposition (SVD) approach to enhance the robustness against round‐off errors. In addition, motivated by the impact of matrix inversion operation on the positive definiteness and symmetry of the error covariance matrix and numerical conditioning, the authors derive a sequential square‐root CKF. The sequential CKF avoids matrix inversion operation involved in the original CKF and directly propagates square factor in each cycle step. The variation is a valid way to better preserve the positive definiteness and symmetry and numerical stability. To evaluate the proposed approaches, four numerical experiments are simulated. The results illustrate the numerical robustness and stability of the proposed approaches.
Phase angle error‐based maximum correntropy adaptation for frequency estimation of three‐phase power system
Authors: Nefabas Gebeyehu L. , Zhao Haiquan , Xia Yili
Published: October 2019

A robust technique based on maximum correntropy criterion (MCC) is proposed for frequency estimation in an unbalanced power system using error in phase angle. This is achieved by replacing the magnitude error used in the standard cost function of the MCC with the phase error and by extending the concept of the augmented (widely linear) complex statistics for the processing of unbalanced voltages and other abnormal system conditions. The idea emanates from the facts that: (i) useful information is primarily conveyed over the phase in power system frequency estimation, and (ii) the performance degradation of the well‐known minimum mean squared error criterion based adaptive filters in impulsive noise environments can be overcome by the MCC due to the non‐quadratic and higher order moments imbedded in its cost function. The proposed phase error‐based MCC method also utilises all the second‐order information within the complex valued system voltage through the Clarke's transformation. Simulation studies conducted using both synthetic and experimental data verify that the proposed algorithm provides better estimations compared to the considered algorithms.
Inductance inrush current time–frequency analysis
Authors: Benšić Tin , Vulin Dragan , Stojkov Marinko , Biondić Ivan
Published: October 2019

This study presents an application of the reassigned Gabor time–frequency transform to the inductance inrush current transient analysis. Measurement setup is constructed and the measurement methodology is presented to obtain fully controlled transformer inrush current measurements. The measured data is analysed by the reassigned Gabor time–frequency transform. The resulting time–frequency distribution of the measured signal shows how inrush DC component and harmonics change with time for different initial conditions. By extracting the stationary points from the reassigned Gabor transform, an algorithm for computing the time duration of the inrush is presented and applied to 88 measured transients.
AC interference from a faulty power line on nearby buried pipelines: influence of the surface layer soil
Authors: Lucca Giovanni
Published: October 2019

The study generalises the calculation method described by Lucca, devoted to the evaluation of induced voltage and current on a pipeline due to a nearby overhead power line in a fault condition, from the case of homogeneous soil to the case of a two‐layer soil. By means of some examples of calculation, it is put in evidence that the homogeneous soil hypothesis can lead, in some cases, to excessively precautionary results and, in other cases, to underestimated results. Therefore, a calculation method based on the two‐layer soil model seems a better approach.
Understanding the dielectric and mechanical properties of self‐passivated Al–epoxy nanocomposites
Authors: Chillu Naresh , Jayaganthan Rengaswamy , Danikas Michael G. , Tanaka Toshikatsu , Sarathi Ramanujam
Published: October 2019

Epoxy nano passivated aluminium composites with optimised size and filler contents were fabricated. Variation in contact angle and surface roughness is insignificant with increasing filler into nanocomposites but it showed a drastic reduction on corona ageing. Water droplet initiated corona inception voltage (CIV) is high under the negative DC voltage followed with positive DC and AC voltages. The bandwidth of ultra‐high frequency signal generated due to water droplet initiated corona discharge lies in the range of 0.5–1.2 GHz. Surface potential measurements have shown that the decay in the potential was fast initially, and it became slower and sluggish subsequently. The trap energy density versus trap depth plot exhibits shallow traps and deep traps at around 0.8 and 0.87 eV, respectively. Permittivity, conductivity and loss factor have increased with an increase in the filler content in nanocomposites. The bulk resistance and capacitance of samples were determined for obtaining the equivalent parallel RC circuit model. Incorporation of nanofillers increases the glass transition temperature and reduces the tan δ with increasing frequencies as evident from dynamic mechanical analysis studies. A direct correlation is observed between the plasma temperature measured through laser‐induced breakdown spectroscopy spectra and hardness of the material.
Chattering‐ free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control
Authors: Karsaz Ali
Published: October 2019

In this study, a closed‐loop control scheme is proposed for the glucose–insulin regulatory system in type‐1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose–insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well‐studied. Higher‐order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non‐linear property of glucose–insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro‐fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG‐level tracking and chattering reduction in the presence of daily glucose‐level disturbances is verified.
Toward fast and accurate emergency cases detection in BSNs
Authors: Boudargham Nadine , El Sibai Rayane , Bou Abdo Jacques , Demerjian Jacques , Guyeux Christophe , Makhoul Abdallah
Published: October 2019

In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples’ lives. Therefore, fast and correct detection of emergencies while maintaining low‐energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.
Improved fault‐tolerant optimal route reconstruction approach for energy consumed areas in wireless sensor networks
Authors: Maratha Priti , Gupta Kapil , Luhach Ashish Kumar
Published: October 2019

Persistent sensing requires sensor nodes to keep functioning for a longer duration. Different amounts of incoming packets on some sensor nodes after deployment lead them to deplete their energy faster compared to other nodes without sending the information about their death to the sink node. It results in the formation of energy holes in the network and hence packet loss problem starts. In this study, this problem is overcome by prior notification to the sink about scarcity of resources by setting a dynamic threshold and also, packet delivery is sustained by using a fault tolerance strategy. Simulation analysis shows that proposed work is doing better in terms of packet delivery ratio and number of dead nodes. Also, network lifetime of the proposed work is found to be 73% of the total time duration while its competitor approach has network lifetime 64%.
Gridless sparsity‐based DOA estimation for sparse linear array
Authors: Zhang Yu , Zhang Gong , Kong Yingying , Wen Fangqing
Published: October 2019

A novel two‐stage gridless sparsity‐based method for sparse linear array direction‐of‐arrival (DOA) estimation is presented. First, the covariance matrix gridless sparse representation method based on atomic norm minimisation is proposed for corresponding structured Toeplitz matrix construction and source number detection. Then, the conventional MUSIC algorithm can be employed for DOA estimation. Compared with conventional subspace‐based algorithms, the proposed two‐stage method can be carried out without knowing source number and directly detect more source signals than sensors. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.
Three‐dimensional surface wave measurement system
Authors: Peng Gantao , Zhu Shitao , Zhang Jingsi , Shi Hongyu , Zhang Yangyang , Li Jianxing , Zhang Anxue
Published: October 2019

This study presents a three‐dimensional near‐field measurement system to measure surface waves using LabVIEW. The system is based on the capacitive coupling between metasurface and the electric probe, which is optimised to increase signal‐matching efficiency. In order to realise three‐dimensional near‐field measurements, the scanner is composed of three‐axis sliding rods and three stepping motors to drive the near‐field probe. Especially, the double position/signal difference method is used to obtain a high spatial resolution. The system is controlled by LabVIEW to realise automatic measurement so that the electric magnitude and phase distribution can be displayed in real time. Finally, a spoof surface plasmon polariton coupling metasurface sample is measured by the system. The measurement results of surface waves are in good agreement with the simulation.
Ground‐based deformation measurement radar configured with multiple‐input multiple‐output antennas
Authors: Wang Haofei , Li Jiandong , Mao Erke , Yang Jian
Published: October 2019

The application of ground‐based radar to monitor the terrain displacement has attracted more and more attentions recently. Most of the existing ground‐based radars utilise a linear horizontal rail where the antennas move to generate the image of the test site. Here, the authors present a topology of antenna array with which the digital elevation model of the observed scene can also be generated besides the 2‐D image. Several field experiments have been carried out to evaluate the performance of authors’ developed ground‐based differential interferometry radar (GB‐DInR) configured with this multiple‐input multiple‐output (MIMO) antenna array. The results demonstrate that authors’ developed MIMO GB‐DInR is able to achieve a sub‐millimetre precision of displacement measurement and derive the elevation information of target with an error of <0.1 m at a distance of around 100 m.
Fusion‐based spectral matching method for hyperspectral target detection
Authors: Wenzheng Wang , Baojun Zhao , Linbo Tang , Shichao Zhou , Fan Feng
Published: October 2019

Target detection using hyperspectral images is one of the most important applications in the field of both civilian and military. Some spectral similarity metric methods have been used in those applications, i.e. spectral angle mapper and spectral gradient angle and so on. However, those methods only consider the similarity between two spectral in one aspect, and many information hidden in the spectrum is ignored. Then, here, the authors propose a new fusion‐based spectral‐matching method for hyperspectral target‐detection task. The proposed Euclidean‐angle matching method combines the magnitude and the shape difference together to determine the similarity between the test and target spectra samples. Authors’ results indicate that the proposed fusion‐based method exhibits a better performance over the other spectral‐matching methods.
Research of InISAR imaging based on joint translational compensation
Authors: Zhang Kai , Hou Yingni , Jin Lin
Published: October 2019

In order to solve the problem that conventional inverse synthetic aperture radar three‐dimensional (InISAR 3D) imaging methods need too much computation and so many restrictions in image registration step, this study proposes an InISAR 3D imaging algorithm based on joint translational compensation. The algorithm employs joint translational compensation of range and cross‐range direction to achieve the aim of imaging and registration. While obtaining the two‐dimensional ISAR images, the accurate registration of different ISAR images is completed. The algorithm needs less calculations and fewer constraints. Simulation results verify the effectiveness of it.
EMD‐based adaptive stationary human imaging for TD‐MIMO through‐wall radar
Authors: Lingxiao Cao , Guolong Cui , Shisheng Guo , Mingyang Wang , Guohao Chen , Ruixin Liu
Published: October 2019

This study develop an adaptive stationary target imaging technique based on the empirical mode decomposition (EMD) method for the time‐division multiple‐input multiple‐output through‐wall radar. By exploiting the discrimination between the respiratory rate and the environmental clutters' frequency, the weak echoes of the stationary target are enhanced via EMD processing adaptively. Based on the simulations as well as the experimental data, the authors verify that the stationary target can be positioned accurately via their proposed method.
Fast and accurate RCS evaluation via high‐performance parallel FDTD simulation
Authors: Zhou Xiao Long , Wang Xin Yu , Zhang Jian Feng , You Jian Wei
Published: October 2019

In this study, a fast and accurate method to predict the radar cross‐section (RCS) of large‐scale and complicated shape targets is proposed based on a high‐performance parallel finite difference time‐domain (FDTD) numerical method. To this end, several most popular parallel computation methods [including OpenMP, graphics processing unit (GPU), and message‐passing interface (MPI)] are discussed first. Based on this discussion, a novel MPI–OpenMP–GPU hybrid parallel computation scheme for FDTD is developed. Moreover, the corresponding load‐balance parallel configuration is discussed as well. Since this hybrid parallel scheme combines the merits of existing parallel technologies, the computation performance is remarkably improved. The results show that the computation time of the RCS simulation of a large‐scale target can be reduced from 3 days to 0.8 h, that is, ∼98.9% time saving.
High‐precision target echo generation technology based on one‐dimensional linear array radar
Authors: Peng Bo , Lan Hetian , Lu Jiaxin , Yang Xiaopeng
Published: October 2019

The high‐precision target echo generation in the field of radar systems is particularly important for large‐scale, comprehensive testing of radars. Conventional echo generation techniques often use planar array air feed to simulate echoes. However, it is complicated and expensive to achieve the echo generation with traditional planar array. Therefore, this study proposes a high‐precision target generation technique based on one‐dimensional linear arrays. The simulation system achieves the target radial high‐precision simulation through DRFM technology; the target azimuth high‐precision simulation is achieved through differential GPS technology; and the target elevation high‐precision simulation is realised through vertical one‐dimensional linear array antenna switching. The semi‐physical simulation system presented here has the advantages of simple control, low cost, low link loss, easy maintenance, and high simulation target accuracy. Finally, the system was tested in the field to verify the theoretical concept of the article.
Nature‐inspired waveform optimisation for range spread target detection in cognitive radar
Authors: Wang Qing , Li Meng , Gao Lirong , Li Kaiming , Chen Hua
Published: October 2019

The waveform optimisation problem in cognitive radar is non‐convex and will have sub‐optimal solutions when solved by the semi‐definite relaxation (SDR) technique. Here, a novel nature‐inspired waveform optimisation framework is proposed for range‐spread target detection in cognitive radar. First, the waveform optimisation problem is formulated using maximum a posteriori probability and Kalman filtering to estimate the target scattering coefficients. To solve this problem more accurately and efficiently, three nature‐inspired algorithms (modified particle swarm optimisation algorithm, Bat Algorithm, and Beetle Antennae Search algorithm), as a nature‐inspired waveform optimisation (NIWO) approach is proposed. It is demonstrated through computer simulations that the proposed NIWO approach significantly outperforms the SDR approach, showing a promising tool for waveform optimisation in cognitive radar.
ISAR imaging resource‐scheduling algorithm in network radar based on information fusion
Authors: Li Aiqiong , Liao Kefei , Ouyang Shan , Li Jingjing
Published: October 2019

Aiming at the application requirements of three‐dimensional (3‐D) fusion imaging of network radar, this study proposes an ISAR imaging resource‐scheduling algorithm for network radar. Based on the feature recognition of each radar to each target, a projection‐based method is first used to fuse the 3‐D information of targets. Then according to the fused information, the demand of time resources for each radar is analysed. The radars selected for each target imaging are determined. Finally, the scheduling model of the imaging task for network radar is established, and time resources for each radar are randomly allocated. The simulation results show that the scheduling success rate of the proposed algorithm is two times on average than that of the traditional algorithm under the situation when the resources are near‐saturated for both algorithms, which can improve the efficiency of network radar.
Ambiguity function analysis of human echolocator waveform by using gammatone filter processing
Authors: Abdullah Raja S.A.R. , Saleh Nur L. , Ahmad Sharifah M.S. , Salah Asem A. , Rashid Nur E.A.
Published: October 2019

Human echolocation is a technique used by small group of people (normally blind persons) to observe their surrounding via emitting an active signal (usually tongue click) and analyse the return echo. This skill allows them to navigate safely by ‘seeing through sound’. Remarkable performances upon echolocating have attracted scholars’ attention from multidiscipline field including psychophysical and engineering. Study of waveform diversity has reported the signal is wideband, consisting of multiple frequency components with exponential decay factor that probably made up the entire unique signal. As such, wideband perspective is considered during ambiguity function analysis using wide‐band ambiguity function method. The waveform analysis continues and reported optimum autocorrelation function is achieved by adopting a bio‐inspired technique, which incorporated gammatone‐filter processing. It will be interesting to synthesise the waveform using gammatone‐filter approaches prior to relay it into ambiguity function process for analysis. Therefore, it is worth to explore its output results in this paper, which hold new findings that may help to understand how these humans achieve an optimum performance upon echolocating including ambiguity function characteristic. Thus, the new knowledge explored in this paper could be beneficial in emerging concept for new development in radar and sonar system application in near future.
Multiscale saliency detection method for ship targets in synthetic aperture radar images
Authors: Yan Cheng Z. , Liu Chang , Pang Ying
Published: October 2019

Ship detection is an important direction of synthetic aperture radar (SAR) image application in maritime surveillance. A multi‐scale optimisation threshold saliency detection is developed in this letter, which is used to detect ships targets of SAR images. The SAR image is first decomposed into a pyramid image sequence. Then the saliency detection is performed using the spectral residual method for each layer in the sequence, and the salient sub‐graph that contains ship targets is obtained. Finally, each sub‐graph is fused and the optimisation threshold segmentation method that applies to the saliency map gives the final detection result. Experimental results show that the proposed approach has low complexity and high detection accuracy and greatly reduces the dependence on prior knowledge.
CRLB for joint estimation of TDOA, phase, FDOA, and Doppler rate
Authors: Hu Dexiu , Chen Shiwen , Bai Hang , Zhao Chuang , Luo Liping
Published: October 2019

To avoid decline of FDOA estimation precision caused by relative Doppler companding, Doppler rate has to be considered and estimated jointly with TDOA, phase, and FDOA. However, CRLB of the joint estimation is still unclear. This study addresses on derivation of the CRLB. First, the joint estimation model is constructed. Then, the signal‐specific CRLB for any type of signal and a simplified CRLB for stationary and constant‐envelope signal are derived, respectively. Finally, as a verification, the derived bound is compared with previous bounds for the special cases of TDOA, phase, and FDOA estimation, which shows that authors’ bound is reasonable. In addition to the conventional scenarios of TDOA, phase and FDOA estimation, their bound can also indicate the error characteristic of Doppler rate, which can be used to evaluate estimation performance and forecast location accuracy using Doppler rate.
Information divergence‐based low probability of intercept waveform detection for multi‐antenna intercept receivers
Authors: Chen Jun , Wang Fei , Zhou Jianjiang , Shi Chenguang
Published: October 2019

Based on the asymptotic spectral distribution of sample covariance matrix, a new low probability of interception (LPI) signal detection method for multi‐antenna intercept receiver is proposed via reforming the noise sequence into a Wishart matrix. The authors deduce the asymptotic cumulative distribution function (CDF) of eigenvalues of sample covariance matrix. Also a numerical Kullback–Leibler divergence of the empirical spectral CDF based on test samples from the deduced asymptotic CDF is established, which is treated as the test statistic. By comparing with other signal detection methods, simulation results show the new LPI signal detection method is superior and robust.
CS‐based processing for high‐resolution passive radar with incoherent dictionary
Authors: Han Jiatong , Bai Xia , Zhao Juan , Shan Tao
Published: October 2019

Passive radar (PR) systems are attractive for various military and commercial applications, where illuminators of opportunity are used. Using the fact that most target scenes are very sparsely populated makes it possible to apply compressive sensing (CS) for PR. CS provides higher resolution than that obtained by using traditional ambiguous function, but suffers from increasing coherence associated with the finer grids whether for monostatic radar or multistatic radar. A novel incoherent CS dictionary construction method is presented here to deal with the case of finer grids. Experimental results demonstrate that the proposed CS‐based processing scheme with incoherent dictionary can work well for high‐resolution PRs.
Development of method to forecast soybean leaf damage by common cutworm using entomological radar and searchlight trap
Authors: Otuka Akira , Matsumura Masaya , Tokuda Makoto
Published: October 2019

To develop a method to forecast soybean leaf damage caused by larvae of the common cutworm, Spodoptera litura (Fabricius), insect dispersion was monitored with an x‐band entomological radar and a searchlight trap during the summer soybean season in 2016 and 2017 in western Japan. The insect catch number in the searchlight trap and number of newly damaged leaves in a soybean field were compared to see how much soybean damage the insect's arrival caused. The 2016 results indicated that catch number peaks appeared 3–5 days before the peaks in the number of newly damaged leaves. The time lag corresponded to the egg period. Similar data were found in 2017. However, both the catch number and the number of newly damaged leaves were smaller that year, due to the lower occurrence of the insect. The radar observation showed dispersion at altitudes over the monitoring site when the peaks in insect catch number occurred. The insects often arrived under a northerly wind condition, from the mountainous area to the north. This study suggested that short‐term forecasting of soybean leaf damage by radar and searchlight trap may be possible.
Generation scheme of chirp scaling phase functions based on floating‐point CORDIC processor
Authors: Fang Linlin , Xie Yizhuang , Li Bingyi , Chen He
Published: October 2019

This study presents a chirp scaling (CS) phase function generation scheme based on CORDIC algorithm, it adopts single precision floating‐point CORDIC processor to implement a variety of non‐linear operations which are involved in CS synthetic aperture radar (SAR) imaging algorithm. We extend the range of convergence of CORDIC algorithm and reduce the data width in rotation unit module by adopting a hardware resource reduction scheme. We also adopt a unified CORDIC processor to achieve square root, multiplication, and division operation, which can substitute multiple single‐function processors and simplify the complicate arithmetic in CS algorithm. As a proof of concept, we verify the performance of the proposed design scheme on Xilinx XC7VX690T FPGA platform, it is also applied to 16384 × 16384 points target SAR imaging system, the FPGA resource occupancy of the CS phase function generation module demonstrates that the usage of CORDIC processor can effectively save the consumption of hardware resources, and the maximum operating frequency is acceptable. The accuracy of phase functions can satisfy the engineering requirements.
Cognitive passive radar system: software defined radio and deep learning approach
Authors: Wang Qing , Du Panfei , Dou Tongdong , Gao Lirong , Li Chun
Published: October 2019

Cognitive passive radar (CPR) system is amenable to electromagnetic environment cooperating and optimal strategy making tasks on account of illumination source availability and waveform evaluation. In fact, intelligence will provide performance gain from multi‐dimension optimisation. Therefore, we propose a multi‐standard multi‐scheme compatible wideband CPR framework which selects the most suitable waveform as the passive radar illumination source. First, we develop a software defined radio platform to realise wideband and flexible signal processing, which provides the feasibility of cognition. Then in order to recognise the electromagnetic waveforms, we present a deep learning framework which can be integrated in CPR system as a classifier using convolutional neural networks (CNNs) and long short‐term memory (LSTM). Via the proposed innovative passive perception‐action cycle, the CPR prototype system demonstrates the real‐time, high accuracy modulation recognition ability and the adaptive signal processing ability. Based on our experiments, it can be demonstrated that our CPR prototype system integrated with deep learning is effective and promising.
Classification for Polsar image based on hölder divergences
Authors: Pan Ting , Peng Dong , Yang Xiangli , Huang Pingping , Yang Wen
Published: October 2019

(Dis)similarity measures play an important role in the interpretation of polarimetric synthetic aperture radar (PolSAR) images. Here, the authors introduce a kind of similarity measures for PolSAR images based on the concepts of Hölder pseudo‐divergence and Hölder divergence. Authors’ similarity measures are more generalised as the derived formulas indicate that they contain several widely used measures, such as Bartlett or Bhattahcharyya distances and Chernoff distance. Also, their similarity measures are derived from the complex Wishart distribution, so these measures are good at quantifying the similarity between two covariance matrices and perform well while dealing with classification problems for PolSAR images. Experimental results on unsupervised and supervised classification of PolSAR images also verify the effectiveness of their measures.
Range sidelobe suppression for OFDM‐integrated radar and communication signal
Authors: Zuo Jiajun , Yang Ruijuan , Luo Shaohua , Zhou Yugang
Published: October 2019

To improve the RadCom system of performing both data transmission and radar‐sensing functions, here a waveform design method for orthogonal frequency division multiplexing (OFDM) signal is proposed to solve the problem of high range sidelobe level in radar detection process while maintaining a reasonable channel capacity. The method only transmits data in the sub‐channels with preferable channel quality. Then, calculate the weight of the chosen subcarriers through the convex optimisation method to traditionally minimise the peak‐to‐sidelobe (PSL) ratio. Simulation results prove that, compared with traditional range sidelobe suppression method, this algorithm is more suitable for RadCom system.
Research on linearisation of power amplifier based on digital pre‐distortion
Authors: Fang Kai , Liu Da , Wang Yongqing
Published: October 2019

The power amplifier is an important device in the communication system, and its non‐linear characteristics will lead to serious distortion of the output signal, reducing the performance of the communication system. In order to solve the problem of non‐linearity in a power amplifier, this study proposes an adaptive learning algorithm based on a digital pre‐distortion structure, which combines direct learning and indirect learning structure. It improves the accuracy of pre‐distorter parameter and has the faster convergence speed. The pre‐distortion device parameters are constantly modified to achieve a good linear effect by using the recursive least square adaptive algorithm. The simulation results show that this structure effectively compensates the output signal distortion of the power amplifier, improves the linearisation degree of the power amplifier and reduces the in‐band distortion and adjacent channel leakage ratio.
Sea clutter suppression algorithm based on signal resonance
Authors: Pan Meiyan , Chen Jianjun , Xie Sudao , Wang Shengli
Published: October 2019

Detecting targets in maritime domain is difficult due to the complex physical mechanism of the sea clutter. A basis pursuit clutter suppression method based on improved tunable Q ‐factor wavelet transform (TQWT) is proposed on the analysis of tunable Q ‐factor. First, the calculation method for selecting an appropriate tunable Q ‐factor is given. Second, the TQWT with selected Q ‐factor is adopted to perform the sparse decomposition for the echo signals to obtain the wavelet coefficients. Then, the ‘Basis Pursuit suppressing clutter’ method is used to optimise coefficients. Finally, the target signal after sea clutter suppression is recovered by inverse TQWT. The authors also explored the influence of regularisation parameter on the algorithm performance which provides basis for the practical engineering applications. The experimental results on the CSIR public dataset demonstrated the effectiveness of the algorithm.
Method for weak target detection in sea environment
Authors: Gao Chang , Tao Ran , Kang Xuejing
Published: October 2019

In this study, a new method for sea surface target detection in low signal‐to‐clutter environment is proposed, which can not only reduce the computation amount but also suppress the sea clutter, static targets and noise. Firstly, the authors use a more general echo signal model, which is more matching for the sea environment. Next, a fast estimation technique with few searching times to reduce the computation of the detection process is put forward. Then, a canceller is designed, which has the merit of suppressing the strong sea clutter, static targets, and noise. Finally, the experiment results demonstrate that the proposed method can achieve high detection probability, fast estimation, and strong suppression ability.
Radar/ESM anti‐bias track association algorithm based on track distance vector detection
Authors: Li Baozhu , Dong Yunlong , Huang Gaodong , Chen Xiaolong , Guan Jian
Published: October 2019

To address radar/ESM track association problem in the presence of sensor biases and different targets reported by different sensors, an anti‐bias track association algorithm based on track distance vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is first derived in MPC. The track distance vectors are obtained by the real state cancellation method. Second, in order to eliminate most non‐homologous target tracks, the rough association is performed according to the features of the azimuthal rate and inverse‐time‐to‐go (ITG). Finally, the track‐to‐track association of radar and ESM is extracted based on track distance vectors chi‐square distribution. The effectiveness of the proposed algorithm are verified by Monte–Carlo simulation experiments in the presence of sensor biases, targets densities, and detection probabilities.
Target separation detection and motion parameter estimation method based on time‐varying autoregressive model
Authors: Zhang Yaolin , Yang Yuhao , Cheng Qiang , Hao Yanjun
Published: October 2019

The authors report a target separation detection and motion parameter estimation method based on the time‐varying autoregressive (TVAR) model. The TVAR model is used to extract the instantaneous Doppler frequencies of multiple targets or scattering points in a same radar range cell, and the motion parameters are estimated based on the Doppler frequencies at every pulse time in a radar frame. In particular, for coherent multipulse echo signal, the TVAR model is first utilised to extract multiple instantaneous Doppler frequencies at each pulse time, thus forming a Doppler frequency matrix. Second, the Doppler tracking and polynomial fitting methods are utilised to estimate the radial velocity and acceleration based on the Doppler frequency matrix. Finally, the target separation detection is achieved by acquiring and validating multiple Doppler frequency components in the same range cell. Simulations and verifications are carried out, and the results show that the proposed method is effective for target separation detection and precision motion parameter estimation, which could be of great value radar target detection, tracking, and automatic target recognition.
SAR image synthesis based on conditional generative adversarial networks
Authors: Wang Jianyu , Li Jingwen , Sun Bing , Zuo Zhixiong
Published: October 2019

In recent years, synthetic aperture radar (SAR) has played an increasingly important role in the military and civil fields. Since the SAR image reflects the scattering characteristics of the target， it is of great significance to achieve multi‐angle fusion of the target. However, there is a problem of angular loss in real SAR images. Through the electromagnetic simulation method, SAR images of 0–360° can be obtained, but the similarity to real images is low. Here, the authors combine electromagnetic simulation with conditional generative adversarial networks (cGANs). The image obtained by the electromagnetic simulation is taken as the input of the cGANs, and then the generator generates photorealistic SAR images containing the label information. Thereby, authors’ method complement the missing angles in the real SAR image dataset. Finally, they qualitatively and quantitatively evaluated the synthetic images generated through their model to verify the quality of the dataset.
Design of low sidelobe random radar signal by frequency‐domain modulation method
Authors: Liu Jiafang , Zhang Yunhua , Dong Xiao
Published: October 2019

A novel method for the design of random radar signal with low sidelobe is proposed in this study. Firstly, the authors modulate the amplitude spectrum of the designed radar signal using a Gaussian window and phase spectrum using a random sequence. Then the inverse Fourier transformation is adopted to obtain the time‐domain designed random radar signal. The random radar signal possesses low sidelobe thanks to its Gaussian power spectral density and random waveform owing to its random phase spectrum. The performance of the designed random radar signal is examined by both numerical simulation and real data experiment with dual‐polarisation radar, where the polarimetric micro‐Doppler of a pedestrian is demonstrated.
Open set HRRP recognition based on convolutional neural network
Authors: Chen Wei , Wang Yanhua , Song Jia , Li Yang
Published: October 2019

Most existing algorithms in high‐resolution range profile recognition focus on the closed set cases, where the test sample is from a known class. However, a sample could be drawn from unknown classes in realistic scenario, which is named as open set recognition. Here, open set HRRP recognition is achieved by incorporating extreme value theory into convolutional neural network. The softmax layer is replaced by a so‐called openmax layer which estimates probabilities of the test sample belonging to known and unknown classes. Experimental results demonstrate that the proposed method outperforms the state‐of‐art algorithms such as NN, 1‐vs‐set machine, and W‐SVM in terms of correct rejection rate.
Hierarchical detection method of specific artificial region using local structural constraint in remote sensing images
Authors: Bi Fukun , Lei Mingyang , Qin Yanyan , Hou Jinyuan , Yang Zhihua , Zhang Jie
Published: October 2019

Automatic detection of specific artificial region is one of the research hotspots in remote sensing image. In this study, a hierarchical detection method of specific artificial region using local structure constraint in remote sensing images is proposed. The process can be divided into two main stages: screening of keypoints based on the description of structural pattern, and specific artificial regions detection based on the local structural similarity. This method can greatly reduce the calculation of feature description in the redundant non‐specific regions, and effectively reduce the error matching points to achieve the rapid and reliable detection of the specific artificial region. The authors test the proposed method on the artificial region sets with different types and time phase. The experimental results show that the method has high computational efficiency and detection accuracy.
Radar HRRP target recognition based on stacked denosing sparse autoencoder
Authors: Tai Guangxing , Wang Yanhua , Li Yang , Hong Wei
Published: October 2019

An end‐to‐end radar high‐resolution range profile recognition method is proposed based on stacked denosing sparse autoencoder which stacks several denosing sparse autoencoders and uses softmax as the classifier. The training process consists of two steps. The first is layer‐by‐layer pre‐training and the second is fine tuning using the pre‐training results for initialisations. The two‐step training process makes this model converge faster and more likely to converge to the global optimal point than directly training the joint network. Experimental result shows that the proposed method achieves higher recognition accuracy than state‐of‐art methods.
Maritime targets classification based on CNN using Gaofen‐3 SAR images
Authors: Ma Mengyuan , Zhang Haojie , Sun Xiaokun , Chen Jie
Published: October 2019

The classification and detection of maritime targets are widely used in shipping navigation and military fields. With the development of spaceborne synthetic aperture radar (SAR) technology, more and more very high‐resolution SAR images can be acquired for maritime targets recognition. However, due to the different imaging mechanisms between SAR images and optical images, it is difficult and laborious to interpret SAR images manually. This study uses a modified Alexnet structure to realise maritime targets classification on the Gaofen‐3 spaceborne SAR images. The maritime targets dataset (MTD), including boats, cargo ships, container ships, windmills, oil tankers, and iron towers, is conducted. Moreover, the proposed convolution neural networks (CNNs) structure is trained and tested on the MTD. Experimental results show that the model can get an accuracy of 92.10% in classifying the six kinds of targets, and the performance is superior compared with other CNNs and traditional supportive vector machine algorithms.
Space‐time adaptive detection for bistatic MIMO system under covariance matrix persymmetry and symmetric spectrum
Authors: Yan Sheng , Hao Chengpeng , Xu Da , Shi Bo
Published: October 2019

Space‐time adaptive detection (STAD) is an important research aspect of the bistatic multiple‐input multiple‐output (MIMO) radar system. The signal and clutter space‐time models of the bistatic MIMO radar are described in this study. The knowledge‐aided (KA) transform, which utilises the structural properties of the target‐free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data.
Application of joint domain localised matrix CFAR detector for HFSWR
Authors: Ye Lei , Yang Qiang , Chen Qiushi , Deng Weibo
Published: October 2019

Target detection is one of the most important parts of high‐frequency surface wave radar (HFSWR) signal processing, to find targets in noise or clutter and obtain targets’ information. However, some factors, such as the influence of clutter and the increase of detection range, will degrade the signal‐to‐clutter ratio (SCR). In the low SCR scenario, the classical detector of HFSWR, which uses the amplitude of receipt signal only, could hardly detect targets. To solve this problem, this study proposes a novel detector called joint domain localised matrix constant false‐alarm rate (CFAR) detector based on receipt signal's multi‐dimensional information. This detector employs joint domain localised algorithm to get signal information in angle and Doppler domain, and use information geometry method to map them to Hermitian positive‐definite (HPD) matrix space which can be depicted as Riemannian manifold. Then, based on HPD matrix, in the range domain the matrix CFAR detector is built to detect targets. The experiments’ results verify that the detector can improve radar detection performance effectively.
Polarimetric radar target recognition framework based on LSTM
Authors: Chen Wei , Zhang Liang , Song Jia , Wang Yanhua , Li Yang
Published: October 2019

Polarimetric information is of great importance for radar target recognition. Conventional polarimetric features are hand‐designed based on scattering mechanism. In this study, a novel polarimetric target recognition framework based on long–short‐term memory (LSTM) network is proposed. The different polarimetric channels are regarded as the sequential inputs in LSTM, and the features are extracted automatically. Experimental results on dual‐polarised high‐resolution range profile recognition demonstrate that the features learnt by LSTM are more discriminating than conventional features. The recognition performance of the proposed method outperforms the state‐of‐the‐art methods as well.