Reviewerly/exHarmony huggingface dataset:
Decentralized Multi-Target Tracking for Netted Radar Systems with Non-Overlapping Field of View

In this paper, a robust and high-accuracy decentral-ized fusion strategy is proposed for multi-target tracking (MTT) in netted radar systems with non-overlapping field of view (FoV). Each radar in the network runs a local Probability Hypothetical Density (PHD) filter with the decentralized consensus protocol to reduce communication bandwidth and eliminate information inconsistency among nodes. In the above process, the most critical core is an effective fusion strategy. Our proposed method adopts the geometric covariance intersection (GCI) rule to improve fusion accuracy. However, the standard GCI fusion is not suitable for the netted radar systems with non-overlapping FoV because it only focuses on the targets within the intersection of radar FoVs. Consider that, we extend the weights in GCI fusion to be a set of state-dependent weights instead of scalars to perform GCI fusion in a more robust manner. Furthermore, the radar FoVs are always unknown and time-varying in practical scenarios. Towards addressing this case, we combine a clustering algorithm based on highest posterior density to maintain a good fusion performance. The Gaussian mixture implementation of the proposed method is provided. Numerical simulations are designed to verify the effectiveness of the proposed method.
The GMCPHD Filter for Irregular Group Target Spawning Based on Star-Convex RHMs

Target spawning and extended shape estimation are important problems in group target tracking. In this paper, we propose a Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter for group targets with spawning and irregular shape based on star-convex Random Hypersurface Model (RHM). In order to solve the problem of irregular group shape, we use star-convex RHM to describe the distribution of measurement sources. Besides, we use the distance division method to realize the division of measurement sets and the judgment of group splitting. On this basis, the real-time tracking of the motion state and extended shape is realized under the framework of GMCPHD. The performance of this algorithm is showcased by comparison with the elliptical RHM-based GMCPHD filter, and the results show that the proposed algorithm can improve the estimation accuracy of group shape and motion state effectively.
Efficient High Degree Cubature Kalman Filters with Reduced Dimension Update

Cubature Kalman Filters (CKF) and other related techniques based on evaluation of non-linear functions at so-called “sigma-points” are a popular and effective modern non-linear state estimation technique. For problems with more severe non-linearity, it is possible to make use of higher degree rules that involve evaluation of the function at increasingly many points. The number of points required for higher degree rules may grow rapidly with the state dimension (sometimes exponentially). This paper introduces a technique by which a cubature rule in the dimension of the measurement may be used for the filter update step rather than a rule in the dimension of the state. This introduces a large potential savings in terms of functional evaluations when the state dimension is large.
SAR Ship Detection Based on An Improved CNN with IoU-FL and Attention Mechanism

Ship detection in synthetic aperture radar (SAR) images is important in the area of marine surveillance. However, the extremely unbalanced distribution of easy and hard examples leads to difficulty in accurately profiling and locating ships in SAR images. In this paper, we propose an improved convolutional neural network (CNN) based on a novel intersection over union (IoU)-embedded-focal loss (IoU-FL) and a convolutional block attention module (CBAM). To reduce the impact of easy background examples and focus on learning hard examples, the focal loss is introduced in the proposed method, during the training process. During the regression process, a novel IoU-embedded- focal loss (IoU-FL) is proposed to mitigate the imbalance problem in bounding box regression (BBR). Moreover, three CBAM modules are embedded in the backbone of the network to refine different level semantic feature maps and suppress unnecessary background ones by focusing on ‘what’ is meaningful in the channel and ‘where’ is informative in the spatial. The proposed method is evaluated on the public SAR ship detection dataset (SSDD) and the results demonstrate that the proposed method is superior to conventional methods in efficiency and accuracy.
RKHS Based Dynamic State Estimator for Non-Gaussian Radar Measurements

In the case of a non-linear system, the dynamic state of the targets (position, velocity, and acceleration) is estimated by an extended Kalman filter (EKF). The theory of EKF is established on the assumption that measurements follow Gaussian distribution. However, in practice, this assumption falls short and limits the application of EKF. In literature, to deal with the non-Gaussianity, the maximum correntropy criterion (MCC)-based EKF (EKF-MCC) has been studied well. The MCC, an information-theoretic criterion, claims to effectively deal with the system's non-Gaussianity. Nevertheless, like EKF, EKF-MCC also approximates the known system non-linearity with a Jacobian. The Jacobian provides the first-order approximation of the non-linearity and hinders the estimation accuracy achieved by EKF-MCC, particularly for complex target motion models. Therefore, in this work, firstly, we propose to use EKF-MCC for estimating the dynamic state of the target from non-Gaussian measurement. After that, utilizing MCC, we propose reproducing kernel Hilbert space (RKHS) based non-linear estimation of system non-linearity and using it with EKF-MCC. Amid non-linear estimation utilizing MCC, the proposed filter is named EKF-MCC-RKHS. The simulation performed to estimate the dynamic states of the complex constant acceleration (CA) target motion model validates the superiority of EKF-MCC-RKHS over recently introduced EKF-MCC and traditional EKF.
Shadow-Oriented Tracking Method for Multi-Target Tracking in Video-SAR

This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully. Shades are low-scattering and varied, resulting in missed tracking. Surroundings can cause interferences, resulting in false tracking. To solve these, we propose a shadow-oriented multi-target tracking method (SOTrack). To avoid false tracking, a pre-processing module is proposed to enhance shadows from surroundings, thus reducing their interferences. To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation. And further, a recall module is designed to recall missed shadows. We conduct experiments on measured data. Results demonstrate that, compared with other methods, SOTrack achieves much higher performance in tracking accuracy-18.4%. And ablation study confirms the effectiveness of the proposed modules.
Explainable Artificial Intelligence based Classification of Automotive Radar Targets

Explainable decision-making is a key component for compliance with regulatory frameworks and winning trust among end users. In this work, we propose to understand the mis-classification of automotive radar images through counterfactual explanations obtained from generative adversarial networks. The proposed method enables perturbations of original radar images belonging to a query class to result in counterfactual images that are classified as the distractor class. The key requirement is that the perturbations must result in realistic images that belong to the original distribution of the query class and also provide physics-based insights into the causes of the misclassification. We test the methods on simulated automotive inverse synthetic aperture radar data images for a query class of a four-wheel mid-size car and a distractor class of a three-wheel auto-rickshaw. Our results show that the shadowing of one or more wheels of the query class is most likely to result in misclassification.
Track-Before-Detect with Kullback-Leibler Divergence Sampling

Track-before-detect (TBD) is a joint detection and tracking approach that takes advantage of a targets motion over time. For most TBD algorithms, the computationally load is very demanding and efficient implementations need to be developed. An algorithm recently proposed for maritime radar is the Bernoulli TBD particle filter with the number of particles determined heuristically. However, this is not a good approach in the maritime domain due to time and range-varying characteristics of sea clutter. In this paper, an efficient TBD algorithm is developed using Kullback Leibler divergence (KLD) sampling to achieve computational efficiency and adaptive selection of the number of particles. Monte Carlo simulations demonstrate that the adaptive selection of particle number results in excellent detection and tracking results.
Multiple Change Point Detection-based Target Detection in Clutter

The effectiveness of target detection methods in radar systems depend on how accurately clutter can be characterized. However, depending on application, clutter statistics vary, and therefore it is difficult to accurately predict such statistics and their parameters. Model-based detection algorithms that are developed for one clutter scenario will fail to yield satisfactory results in another scenario. In this paper, we propose a complete data driven multiple change point detection (CPD) for target detection which does not requires the knowledge of the underlying clutter distribution. The key concept is to iteratively search for slow time instance that maximizes the cumulative sum (CUMSUM) Kolmogorov-Smirnov (KS) statistics. If such statistics exceeds a pre-specified threshold value, then this slow time instance is added to the collection of the estimated change points. This process continues until all CUMSUM-KS statistics are below the threshold. Computer simulations are used to demonstrate the effectiveness of this method for different clutter distributions.
A Nonlinear Sum of Squares Search for CAZAC Sequences

Wereport on a search for CAZAC sequences by using a nonlinear sum of squaresoptimization proceedure. Up to equivalence, we found all length 7 CAZACsequences. We obtained evidence suggesting there are finitely many length 10CAZAC sequences with a total of 3040 sequences. Last, we compute longer CAZACsequences and compare their aperiodic autocorrelation properties to thewell-known Zadoff-Chu and Björck sequences. The code and results of this searchare publicly available through GitHub.
Practical Considerations for Optimal Mismatched Filtering of Nonrepeating Waveforms

We consider the intersection between nonrepeating random FM (RFM) waveforms and practical forms of optimal mismatched filtering (MMF). Specifically, the spectrally-shaped inverse filter (SIF) is a well-known approximation to the least-squares (LS-MMF) that provides significant computational savings. Given that nonrepeating waveforms likewise require unique nonrepeating MMFs, this efficient form is an attractive option. Moreover, both RFM waveforms and the SIF rely on spectrum shaping, which establishes a relationship between the goodness of a particular waveform and the mismatch loss (MML) the corresponding filter can achieve. Both simulated and open-air experimental results are shown to demonstrate performance.
Semi-Supervised Active Learning for Radar based Object Classification Using Track Consistency

Development of machine learning (ML) models requires large amounts of labeled data. For safety critical automotive applications such as radar based perception, the dataset must contain various and rare corner cases, e.g. rare instances that have not been seen before. The straightforward approach of measuring and manually labeling large amounts of data to capture such corner cases is often infeasible or impractical. Thus, approaches for efficiently selecting and labeling the relevant data are essential for ML-based radar applications. In this paper, we propose a method for semi-supervised learning (SSL) for radar object type classification. We use the track consistency of tracked radar objects as a constraint to generate high-quality labels for the vast portions of the unlabeled dataset. We extend the proposed SSL approach with active learning that considers the data relevance, such that the most relevant data with the least accurate auto-labels are selected for human labeling. We show that the proposed approach achieves a saving of more than 87% of human labeling costs based on auto-labeling and relevant data selection.
Fusion of Asynchronous Radar and Infrared Sensors Data on a Moving Platform Using the PHD Filter

We consider heterogeneous asynchronous sensor fusion of radar and infrared (IR) data on a moving platform. Existing work seldom considers heterogeneous and asynchronous problems simultaneously. First, we build models for radar and IR sensors. Since they have different coordinates, dimensions and data rates, some valid solutions are proposed in this paper. The unscented transformation (UT) is used to unify their coordinates. We use a batch processing method to handle the asynchronous arrival time. For efficient multitarget tracking (MTT), we extend the heterogeneous trackers into the Probability Hypothesis Density (PHD) filter even though their models are distinct. In view of the unknown dependence between sensors on the same platform, the local posteriors are fused using the Generalized Covariance Intersection (GCI) rule. Furthermore, combined with the distinct models, a concrete form of heterogeneous asynchronous GCI rule is derived. Finally, numerical results confirm the excellent performance of the proposed fusion scheme.
TripleM:Multidimensional Feature Separation of Multiple Gestures in Multi-Person Scenarios Based on FMCW Radar

A remarkable process has been made in hand gesture recognition based on radar sensors, which has potential application in human-computer interaction. However, existing research is mostly conducted for only one hand gesture in the sensor's field of view, leading to limitations in some practical scenarios when multiple gestures exist simultaneously. In this paper, we propose a method to estimate the number of multiple gestures precisely in multi-person scenarios firstly, then separates the mixed blind source signals to acquire each motion signal. Finally, we extract multidimensional features for each separated gesture. Extensive experiments are carried out based on commer-cial frequency modulated continuous wave (FMCW) single-input multi-output (SIMO) millimeter-wave radar systems to verify the effectiveness of this approach.
Surveillance Performance of Digital Radars in Non-uniform Target Behavior

Surveillance radar systems generally do not use knowledge of the operating environment to adapt operation. In situations with non-uniform target behavior, failure to consider such operating environment knowledge will lead to sub-optimal performance. In this work, using a surveillance-inspired task, we demonstrate the feasibility and benefit of adapting digital radar operation using operating environment knowledge. The core idea is to use a simulated environment that has been configured with a priori knowledge about target behavior in order to generate training data that will allow a neural network to accurately predict which area of the environment to illuminate. The simulated environment allows the radar system to explore the most rewarding solution to different possible states and the neural network provides generalization, enabling solutions on previously unseen states. The results obtained show improvement over the state of practice and demonstrate the feasibility of the proposed solution approach.
Velocities in Human Hand Gestures for Radar-based Gesture Recognition Applications

Gesture recognition is a simple and intuitive way of human-machine interaction that is used more and more often to control devices in a variety of applications. Often, the gestures are captured by a radar sensor due to its robustness to challenging lighting conditions and high privacy capability. To tailor the sensor parametrization to the use in gesture recognition, the expectable velocities, amongst other parameters, have to be known. An investigation with a set of 15 defined gestures plus one individually chosen movement and 25 participants was carried out to find the main dependencies for these velocities and give an outline on how to approach radar parametrization concerning velocities for gesture recognition applications. The gesture set and setup of a certain application were identified to be the most relevant design choices, from which the expectable velocities can be determined with just a few test participants.
The Smart Development with Dynamic Recollection Adaptive Loom for Frequent Pattern Monitoring in Large Scale Databases

The Dynamic Recollection Adaptive Loom (DRAL) is a groundbreaking technology that provides real-time monitoring and analysis of frequent patterns in data streams. This technology is based on the concept of dynamic memory, which allows the system to quickly adapt to changing patterns and data flows and automatically adjust to new patterns and trends. DRAL is designed to provide a comprehensive and efficient way of detecting, analyzing and responding to frequent patterns in data streams. It uses a combination of machine learning algorithms and data mining techniques to accurately detect and analyze patterns in data streams. This technology is able to rapidly detect outliers and anomalies in the data stream and quickly identify frequent patterns. Additionally, it can quickly respond to changes in the data stream and provide datadriven recommendations for optimization and future predictions. DRAL also provides a robust and secure data management platform that enables users to securely store and manage their data streams in a secure and efficient manner. This technology also provides a comprehensive security framework that ensures the confidentiality and integrity of the data streams. It enables users to easily monitor and manage their data streams and quickly respond to any changes.
Reinforcement Learning Approach for a Cognitive Framework for Classification

The article presents an online learning approach for a cognitive framework for classification. The classification process is realised via sequential illuminations with different waveforms and modelled by partially observable Markov decision processes. Since the operational environment is not accessible, the agent is trained on a similar one. The difference between the trained and operational environment is learned without external knowledge and an existing model. In a first step, the capability of this new approach is shown on generic data. In a second step, it is used on high fidelity simulated data and different waveforms that create high resolution range profiles. The tests on the scenario with electromagnetic models, show noticeable improvement in comparison to the framework without learning capability.
Design and Implementation of Event Knowledge Graph Construction Platform Based on Neo4j

The traditional knowledge graph research field focuses on static knowledge such as entities and entity relationships. Events are dynamic and have the characteristics of actions, participants, and time and space. It is a coarse-grained way of knowledge representation. This paper first uses Python's xml.dom module to parse the marked corpus text in the Chinese emergency corpus CEC to obtain event semantic information, and then realizes the complete mapping of event semantic information to Neo4j graph database to store knowledge graphs, and finally designs and implements events A knowledge graph platform, which can construct the event knowledge in the database into an event knowledge graph, and complete the basic functions of adding, deleting, modifying, and checking event nodes and event relationships. The research in this paper can provide favorable support for event-oriented knowledge processing applications.
Gradient Boost Decision Tree Classifier Based Big Data Analysis for Twitter Sentiment Analysis Using Adaptive Neural Network

Social media in nowadays has a lot of data to improve, leading to legacy organizations as a tag for big data and failing to manage this data. Large volume data management is now an issue. Big data growth is huge. Due to various properties, it is very difficult to manage. This manuscript focuses on analysing big data in a Twitter environment using Hadoop. We categorize big data according by the characteristics huge data, value, diversity and speed. This action involves tokenization, removal of blocked, ignored word, and numerous deletions. Beginning Word Words HDFS (Hadoop Distributed File System) Passes to reduce repetitive words and delete using graph and reduction techniques. After that, emoticons and nonemoticons features are obtained. The resulting properties rank their subject. Next, the classification is done using DLMNN (Deep Learning Modified Neural Network) which is based on Improved Elephant herd optimization for Weight optimization. Finally, the result achieved was verified using the analysis method cross-validation method. The final contribution is made with the help of an efficient Sentiment Analysis (SA) and Twitter Data Awareness Classification a Gradient Boost Decision Tree Classifier (GBDT). Finally, GBDT classifies the data has highperformance to categorize the classes negative, positive, or neutral. In addition, we propose a very effective impression result based on Emotional analysis like this has proven to be very effective and accurate.
A Novel Method of Support Vector Machine for the Evaluation and Optimization of Financial Management

Evaluation optimization is the major content of financial management, but in the evaluation and optimization process, the number of financial data and evaluation means will affect the outputs, decrease the accuracy of evaluation optimization, and lead to errors in optimization results. Rest on this, this paper develops a support vector machine technique to evaluate and optimize financial management, maximize the level of financial management, and shrink the evaluation and optimization time. Then, a comprehensive assessment of financial management is carried out. Finally, vector computing manages evaluation optimization and outputs the optimization outputs. The simulation outputs of MATLAB represent that the support vector machine method can accurately evaluate and optimize, improve the level of financial management, and the evaluation and optimization accuracy is more significant than 92%, which is better than the vector calculation method. At the same time, the results of the support vector machine technique developed in this paper have a small change range, less than 3.6%, which is superior to the original calculation method. In addition, in the financial management, support vector machine method for data processing time is less than 5 seconds, better than manual evaluation method. Therefore, the support vector machine method can meet the evaluation necessity of financial management and is perfect for optimal financial management analysis.