IEEE Transactions on Radar Systems最新文献_第8页

Latent Variable and Classification Performance Analysis of Bird–Drone Spectrograms With Elementary Autoencoder 基于初级自编码器的鸟-无人机谱图潜变量及分类性能分析

IEEE Transactions on Radar Systems Pub Date : 2024-12-17 DOI: 10.1109/TRS.2024.3518842

Daniel White;Mohammed Jahangir;Amit Kumar Mishra;Chris J. Baker;Michail Antoniou

{"title":"Latent Variable and Classification Performance Analysis of Bird–Drone Spectrograms With Elementary Autoencoder","authors":"Daniel White;Mohammed Jahangir;Amit Kumar Mishra;Chris J. Baker;Michail Antoniou","doi":"10.1109/TRS.2024.3518842","DOIUrl":"https://doi.org/10.1109/TRS.2024.3518842","url":null,"abstract":"Deep learning with convolutional neural networks (CNNs) has been widely utilized in radar research concerning automatic target recognition. Maximizing numerical metrics to gauge the performance of such algorithms does not necessarily correspond to model robustness against untested targets, nor does it lead to improved model interpretability. Approaches designed to explain the mechanisms behind the operation of a classifier on radar data are proliferating, but bring with them a significant computational and analysis overhead. This work uses an elementary unsupervised convolutional autoencoder (CAE) to learn a compressed representation of a challenging dataset of urban bird and drone targets, and subsequently if apparent, the quality of the representation via preservation of class labels leads to better classification performance after a separate supervised training stage. It is shown that a CAE that reduces the features output after each layer of the encoder gives rise to the best drone versus bird classifier. A clear connection between unsupervised evaluation via label preservation in the latent space and subsequent classification accuracy after supervised fine-tuning is shown, supporting further efforts to optimize radar data latent representations to enable optimal performance and model interpretability.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"115-123"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Train Offline, Refine Online: Improving Cognitive Tracking Radar Performance With Approximate Policy Iteration and Deep Neural Networks 离线训练，在线改进：用近似策略迭代和深度神经网络改进认知跟踪雷达性能

IEEE Transactions on Radar Systems Pub Date : 2024-12-17 DOI: 10.1109/TRS.2024.3518954

Brian W. Rybicki;Jill K. Nelson

{"title":"Train Offline, Refine Online: Improving Cognitive Tracking Radar Performance With Approximate Policy Iteration and Deep Neural Networks","authors":"Brian W. Rybicki;Jill K. Nelson","doi":"10.1109/TRS.2024.3518954","DOIUrl":"https://doi.org/10.1109/TRS.2024.3518954","url":null,"abstract":"A cognitive tracking radar continuously acquires, stores, and exploits knowledge from its target environment in order to improve kinematic tracking performance. In this work, we apply a reinforcement learning (RL) technique, API-DNN, based on approximate policy iteration (API) with a deep neural network (DNN) policy to cognitive radar tracking. API-DNN iteratively improves upon an initial base policy using repeated application of rollout and supervised learning. This approach can appropriately balance online versus offline computation in order to improve efficiency and can adapt to changes in problem specification through online replanning. Prior state-of-the-art cognitive radar tracking approaches either rely on sophisticated search procedures with heuristics and carefully selected hyperparameters or deep RL (DRL) agents based on exotic DNN architectures with poorly understood performance guarantees. API-DNN, instead, is based on well-known principles of rollout, Monte Carlo simulation, and basic DNN function approximation. We demonstrate the effectiveness of API-DNN in cognitive radar simulations based on a standard maneuvering target tracking benchmark scenario. We also show how API-DNN can implement online replanning with updated target information.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"57-70"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthetic Radar Signal Generator for Human Motion Analysis 人体运动分析合成雷达信号发生器

IEEE Transactions on Radar Systems Pub Date : 2024-12-17 DOI: 10.1109/TRS.2024.3519138

Evert I. Pocoma Copa;Hasan Can Yildirim;Jean-François Determe;François Horlin

{"title":"Synthetic Radar Signal Generator for Human Motion Analysis","authors":"Evert I. Pocoma Copa;Hasan Can Yildirim;Jean-François Determe;François Horlin","doi":"10.1109/TRS.2024.3519138","DOIUrl":"https://doi.org/10.1109/TRS.2024.3519138","url":null,"abstract":"Synthetic generation of radar signals is an attractive solution to alleviate the lack of standardized datasets containing paired radar and human-motion data. Unfortunately, current approaches in the literature, such as SimHumalator, fail to closely resemble real measurements and thus cannot be used alone in data-driven applications that rely on large training sets. Consequently, we propose an empirical signal model that considers the human body as an ensemble of extended targets. Unlike SimHumalator, which uses a single-point scatterer, our approach locates a multiple-point scatterer on each body part. Our method does not rely on 3-D-meshes but leverages primitive shapes fit to each body part, thereby making it possible to take advantage of publicly available motion-capture (MoCap) datasets. By carefully selecting the parameters of the proposed empirical model, we can generate Doppler-time spectrograms (DTSs) that better resemble real measurements, thus reducing the gap between synthetic and real data. Finally, we show the applicability of our approach in two different application use cases that leverage artificial neural networks (ANNs) to address activity classification and skeleton-joint velocity estimation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"88-100"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation of Precipitation Echoes From Airborne Dual-Polarization Weather Radar Based on a Fast Algorithm for Invariant Imbedding T-Matrix 基于快速不变嵌入t矩阵算法的机载双极化天气雷达降水回波模拟

IEEE Transactions on Radar Systems Pub Date : 2024-12-13 DOI: 10.1109/TRS.2024.3516745

Hai Li;Yu Xiong;Boxin Zhang;Zihua Wu

{"title":"Simulation of Precipitation Echoes From Airborne Dual-Polarization Weather Radar Based on a Fast Algorithm for Invariant Imbedding T-Matrix","authors":"Hai Li;Yu Xiong;Boxin Zhang;Zihua Wu","doi":"10.1109/TRS.2024.3516745","DOIUrl":"https://doi.org/10.1109/TRS.2024.3516745","url":null,"abstract":"Modeling nonspherical precipitation targets and calculating their scattering properties are key for simulating dual-polarization weather radar echoes and remote sensing. The invariant imbedding T-matrix (IITM) method, due to its accuracy and practicality in computing nonspherical precipitation targets, is the most promising approach. However, accurate echo simulation requires repeated calculations of the scattering amplitude matrices for precipitation targets at various diameters, involving iterative computations, which leads to significant memory usage and long computation times when using the IITM. Hence, enhancing the computational efficiency of the IITM in simulations of nonspherical precipitation targets in dual-polarization weather radars is urgent. This article improves upon the traditional method of using ellipsoids for modeling precipitation targets by precisely considering particle shapes, employing various nonspherical particles, and dividing these targets into an inscribed homogeneous domain and an extended heterogeneous domain. For the homogeneous domain, the logarithmic-derivative Mie scattering method is used to improve computational efficiency, while the heterogeneous domain utilizes conventional iterative methods, rotational symmetry fast algorithms, and N-fold symmetry fast algorithms. The computed scattering amplitude matrices are integrated with the weather radar equation and pulse covariance matrix to complete echo simulations. Analyzing the computational results from individual particles and overall calculations, experiments show that fast algorithms can increase the computational efficiency of simulating various nonspherical precipitation targets in airborne dual-polarization weather radars by more than tenfold.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"135-154"},"PeriodicalIF":0.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FMCW Radar-Based Drowsiness Detection With a Convolutional Adaptive Pooling Attention Gated-Recurrent-Unit Network 基于FMCW雷达的卷积自适应池化注意力门控递归单元网络睡意检测

IEEE Transactions on Radar Systems Pub Date : 2024-12-12 DOI: 10.1109/TRS.2024.3516413

Wending Li;Zhihuo Xu;Liu Chu;Quan Shi;Robin Braun;Jiajia Shi

{"title":"FMCW Radar-Based Drowsiness Detection With a Convolutional Adaptive Pooling Attention Gated-Recurrent-Unit Network","authors":"Wending Li;Zhihuo Xu;Liu Chu;Quan Shi;Robin Braun;Jiajia Shi","doi":"10.1109/TRS.2024.3516413","DOIUrl":"https://doi.org/10.1109/TRS.2024.3516413","url":null,"abstract":"The state of drowsiness significantly affects work efficiency and productivity, increasing the risk of accidents and mishaps. Radar-based detection technology offers significant advantages in drowsiness detection, providing a noninvasive and reliable method based on vital sign tracking and physiological feature extraction. However, the classification of sleepiness levels is often simple and the detection accuracy is limited. This study proposes a frequency-modulated continuous-wave (FMCW) radar-based system with a convolutional adaptive pooling attention gated-recurrent-unit (CAPA-GRU) network to enhance detection accuracy and precisely determine levels of radar-based drowsiness detection. First, an FMCW radar is used to obtain breathing and heartbeat signals, and the radar signals are processed through the wavelet transform method to obtain highly accurate physiological characteristics. Then, the vital sign signals are analyzed both in the time and frequency domains, and the optimal input data is obtained by combining the characteristic data. Also, the CAPA-GRU, comprising a convolutional neural network (CNN), a gated-recurrent-unit (GRU), and a convolutional adaptive average pooling (CAA) module, is proposed for drowsiness classification and monitoring. The experimental results show that the proposed method achieves multistage sleepiness detection based on FMCW radar and achieves excellent results in low classification. The proposed network has excellent performance and certain robustness. Experiments conducted with cross-validation on a self-collected dataset show that the proposed method achieved 90.11% accuracy in binary classification, 80.50% accuracy in ternary classification, and 58.17% accuracy in quinary classification and the study also used a public data set for sleepiness detection, and the detection accuracy reached 97.34%.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"71-87"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar HOOD：利用FMCW雷达实时检测人的存在和分布

IEEE Transactions on Radar Systems Pub Date : 2024-12-11 DOI: 10.1109/TRS.2024.3514840

Sabri Mustafa Kahya;Muhammet Sami Yavuz;Eckehard Steinbach

{"title":"HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar","authors":"Sabri Mustafa Kahya;Muhammet Sami Yavuz;Eckehard Steinbach","doi":"10.1109/TRS.2024.3514840","DOIUrl":"https://doi.org/10.1109/TRS.2024.3514840","url":null,"abstract":"Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutters. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60-GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro- and micro-range-Doppler images (RDIs). HOOD aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. Since HOOD is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans’ absence and predicts the current scene’s output as “no presence.” HOOD performs well in diverse scenarios, demonstrating its effectiveness across different human activities and situations. On our dataset collected with a 60-GHz short-range FMCW radar with only one transmit (Tx) and three receive antennas, we achieved an average area under the receiver operating characteristic curve (AUROC) of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Importantly, HOOD also perfectly fits on Raspberry Pi 3B+ with a advanced RISC machines (ARM) Cortex-A53 CPU, which showcases its versatility across different hardware environments. Videos of our human presence detection experiments are available at: \u0000<uri>https://muskahya.github.io/HOOD</uri>\u0000.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"44-56"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Radar Systems Publication Information IEEE雷达系统出版信息汇刊

IEEE Transactions on Radar Systems Pub Date : 2024-12-11 DOI: 10.1109/TRS.2024.3500857

引用次数: 0

Interference Resilient Integrated Sensing and Communication Using Multiplexed Chaos 基于多路复用混沌的抗干扰集成传感与通信

IEEE Transactions on Radar Systems Pub Date : 2024-12-09 DOI: 10.1109/TRS.2024.3513293

Chandra S. Pappu;Sonny Grooms;Dmitriy Garmatyuk;Thomas L. Carroll;Aubrey N. Beal;Saba Mudaliar

{"title":"Interference Resilient Integrated Sensing and Communication Using Multiplexed Chaos","authors":"Chandra S. Pappu;Sonny Grooms;Dmitriy Garmatyuk;Thomas L. Carroll;Aubrey N. Beal;Saba Mudaliar","doi":"10.1109/TRS.2024.3513293","DOIUrl":"https://doi.org/10.1109/TRS.2024.3513293","url":null,"abstract":"The increased usage of wireless services in the congested electromagnetic spectrum has caused communication systems to contend with the existing operational radar frequency bands. Integrated sensing and communication (ISAC) systems that share the same frequency band and signaling strategies, such as a single radio frequency emission, address these congestion issues. In this work, we propose novel chaotic signal processing techniques and waveform design methods for ISAC systems. First, we consider a family of chaotic oscillators and use their output to encode the information. Next, we multiplex the information carrying chaotic signals to improve the data rates significantly and further use it for ISAC transmission. We show that a simple correlator can accurately decode the information with low bit-error rates. The performance of the multiplexed waveform is robust in the Rician multipath channel. Using correlation and ambiguity function analysis, we claim that the proposed waveforms are excellent candidates for high-resolution radar imaging. We generate synthetic aperture radar (SAR) images using the backprojection algorithm (BPA). The SAR images generated using multiplexed chaos-based waveforms are of similar quality compared to traditionally used linear frequency-modulated waveforms. The most important feature of the proposed multiplexed chaos-based waveforms is their inherent resilience to intentional and nonintentional interference.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"26-43"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10781422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction of Extended Target Intensity Maps and Velocity Distribution for Human Activity Classification 重构扩展目标强度图和速度分布以进行人类活动分类

IEEE Transactions on Radar Systems Pub Date : 2024-12-02 DOI: 10.1109/TRS.2024.3509775

Nicolas C. Kruse;Ronny G. Guendel;Francesco Fioranelli;Alexander Yarovoy

引用次数: 0

A Learning Bayesian MAP Framework for Joint SAR Imaging and Target Detection 用于联合合成孔径雷达成像和目标探测的学习贝叶斯 MAP 框架

IEEE Transactions on Radar Systems Pub Date : 2024-11-13 DOI: 10.1109/TRS.2024.3497057

Hongyang An;Jianyu Yang;Yuping Xiao;Min Li;Haowen Zuo;Zhongyu Li;Wei Pu;Junjie Wu

{"title":"A Learning Bayesian MAP Framework for Joint SAR Imaging and Target Detection","authors":"Hongyang An;Jianyu Yang;Yuping Xiao;Min Li;Haowen Zuo;Zhongyu Li;Wei Pu;Junjie Wu","doi":"10.1109/TRS.2024.3497057","DOIUrl":"https://doi.org/10.1109/TRS.2024.3497057","url":null,"abstract":"In synthetic aperture radar (SAR) information acquisition, target detection is often performed on the basis of the acquired radar images. Under low signal-to-clutter ratio (SCR) or low signal-to-noise ratio (SNR) conditions, detection by images is likely to cause loss of targets. To address this problem, we propose a joint imaging and target detection network based on Bayesian maximum a posteriori (MAP) estimation. The imaging and detection results are, respectively, defined as scene magnitude and detection label, and their joint probability distribution is used in place of the distribution of scene magnitudes. In the MAP estimation, the continuity feature of the detection label is merged into the optimization process, and the imaging and detection results are optimized alternately to get an iterative solution. The iterative solution is then unrolled into a network, which consists of three modules. We first utilize the unrolled fast iterative shrinkage thresholding algorithm (FISTA) method for the image formation module and then incorporate the detection label estimation module and distribution parameter updating module to learn the detection label and the function of distribution parameters. This approach applies prior information for both imaging and detection processes and enables automatic learning of parameters that are difficult to fit. Simulation experiments demonstrate that the method can simultaneously achieve imaging and target detection under strong clutter and strong noise conditions, showing superior performance in both aspects.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1214-1228"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0