IEEE Transactions on Radar Systems最新文献_第7页

Machine Learning-Aided Nonhomogeneity Detection Method for Airborne Radar 基于机器学习的机载雷达非均匀性检测方法

IEEE Transactions on Radar Systems Pub Date : 2025-01-10 DOI: 10.1109/TRS.2025.3528032

Zeyu Wang;Hongmeng Chen;Shuwen Xu;Ming Li

{"title":"Machine Learning-Aided Nonhomogeneity Detection Method for Airborne Radar","authors":"Zeyu Wang;Hongmeng Chen;Shuwen Xu;Ming Li","doi":"10.1109/TRS.2025.3528032","DOIUrl":"https://doi.org/10.1109/TRS.2025.3528032","url":null,"abstract":"The weight vector in space-time adaptive processing (STAP) algorithm will lead to notches at the position of the interfering targets when there are interfering targets in the training data. If these interfering targets are close to the target of interest on the space-time spectrum, the target signal self-nulling occurs. To deal with this problem, a machine learning-aided nonhomogeneity detection (ML-NHD) method is proposed. More specifically, the subaperture smoothing technique is first performed on each training data to obtain the subaperture sample covariance matrices (SCMs). We prove that when the airborne radar works in side-looking mode and the clutter foldover factor is an integer, the numbers of large eigenvalues (EIGs) of the subaperture SCMs are different for the ordinary training data samples and outlier training data samples. Then, four features are constructed based on the differences in the characteristics of EIGs and eigenvectors of the subaperture SCMs. Finally, a binary classifier based on support vector machine (SVM) is trained to classify the ordinary training data and the outlier training data. The performance assessment shows that the ML-NHD method can detect the outlier training data effectively and achieves better performance of clutter suppression compared with the conventional methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"220-232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105938","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

Classification of Dynamic Vulnerable Road Users Using a Polarimetric mm-Wave MIMO Radar 利用极化毫米波MIMO雷达对动态弱势道路使用者进行分类

IEEE Transactions on Radar Systems Pub Date : 2025-01-09 DOI: 10.1109/TRS.2025.3527884

Wietse Bouwmeester;Francesco Fioranelli;Alexander G. Yarovoy

{"title":"Classification of Dynamic Vulnerable Road Users Using a Polarimetric mm-Wave MIMO Radar","authors":"Wietse Bouwmeester;Francesco Fioranelli;Alexander G. Yarovoy","doi":"10.1109/TRS.2025.3527884","DOIUrl":"https://doi.org/10.1109/TRS.2025.3527884","url":null,"abstract":"In this article, the classification of dynamic vulnerable road users (VRUs) using polarimetric automotive radar is considered. To this end, a signal processing pipeline for polarimetric automotive MIMO radar is proposed, including a method to enhance angular resolution by combining data from all polarimetric channels. The proposed signal processing pipeline is applied to measurement data of three different types of VRUs and a car, collected with a custom automotive polarimetric radar, developed in collaboration with Huber+Suhner AG. Several polarimetric features are estimated from the range-velocity signatures of the measured targets and are subsequently analyzed. A Bayesian classifier and a convolutional neural network (CNN) using these estimated polarimetric features are proposed and their performance is compared against their single-polarized counterparts. It is found that for the Bayesian classifier, a significant increase in classification performance is achieved, compared to the same classifier using single polarized information. For the CNN-based classifier, utilizing the distribution of polarimetric features of the target’s range-velocity signatures also increases classification performance, compared to its single-polarized version. This shows that polarimetric information is valuable for classification of VRUs and objects of interest in automotive radar.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"203-219"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105963","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

Enhanced Weather Surveillance Capabilities With Multiple Simultaneous Transmit Beams 通过多个同时发射波束增强天气监视能力

IEEE Transactions on Radar Systems Pub Date : 2025-01-09 DOI: 10.1109/TRS.2025.3527882

David Schvartzman;Robert D. Palmer;Matthew Herndon;Mark B. Yeary

{"title":"Enhanced Weather Surveillance Capabilities With Multiple Simultaneous Transmit Beams","authors":"David Schvartzman;Robert D. Palmer;Matthew Herndon;Mark B. Yeary","doi":"10.1109/TRS.2025.3527882","DOIUrl":"https://doi.org/10.1109/TRS.2025.3527882","url":null,"abstract":"Phased array radar (PAR) represents the future of polarimetric weather surveillance, driven by the need for high-temporal resolution observations to improve storm monitoring and precipitation analysis. This study presents a novel technique for generating multiple simultaneous transmit beams using phase-only beamforming weights. Unlike previous methods, this approach generates multiple narrow and separate transmit peaks, minimizing sensitivity loss (compared to broadened beams) and improving sidelobe isolation. Bézier surfaces are used to parametrize the element-level phases across the array, producing a smooth distribution with reduced optimization complexity. This article outlines the theoretical formulation, demonstrates simulation results of the phase-only optimization, and validates the method with experimental data collected with the fully digital Horus PAR. Validation using a point target revealed precise beam pointing with angular accuracy within <inline-formula> <tex-math>$0.1^{circ },$ </tex-math></inline-formula>, and measurements during a severe weather event resulted in high-quality polarimetric measurements. Scatterplots comparing the Horus radar data to that from the KCRI [Weather Surveillance Radar—1988 Doppler (WSR-88D)] radar show high correlations (e.g., reflectivity correlation coefficient of 0.91), underscoring the accuracy and reliability of the approach. These findings highlight the potential of multiple simultaneous beams for the next-generation weather radar systems, enabling high-temporal resolution observations and advanced capabilities for weather surveillance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"272-289"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10835246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105943","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

Transformer-Based Automatic Target Recognition for 3D-InISAR 基于变压器的3D-InISAR自动目标识别

IEEE Transactions on Radar Systems Pub Date : 2025-01-08 DOI: 10.1109/TRS.2025.3527281

Giulio Meucci;Elisa Giusti;Ajeet Kumar;Francesco Mancuso;Selenia Ghio;Marco Martorella

{"title":"Transformer-Based Automatic Target Recognition for 3D-InISAR","authors":"Giulio Meucci;Elisa Giusti;Ajeet Kumar;Francesco Mancuso;Selenia Ghio;Marco Martorella","doi":"10.1109/TRS.2025.3527281","DOIUrl":"https://doi.org/10.1109/TRS.2025.3527281","url":null,"abstract":"The 3-D interferometric inverse synthetic aperture radar (3D-InISAR) imaging provides a more complete and reliable representation of targets compared to traditional 2D-ISAR, overcoming limitations related to the geometry of the radar-target system and relative motion. This article presents the application of a point cloud transformer (PCT) for automatic target recognition (ATR) using 3D-InISAR data. The PCT model, originally developed to classify LIDAR’s point clouds, is trained on sparse synthetic point cloud datasets representing various military vehicles, including cars, tanks, and trucks. The synthetic data are carefully generated from computer-aided design (CAD) models, incorporating techniques such as voxel downsampling and data augmentation to ensure high fidelity and diversity. Initial testing on synthetic data demonstrates the PCT’s robustness and high accuracy when used for ATR. To bridge the gap between synthetic and real data, a transfer learning approach is employed, which operates a fine-tuning on the pretrained model by using real 3D-InISAR point clouds obtained from the publicly available sensor data management system (SDMS)-Air Force Research Laboratory (AFRL) dataset. Results show significant improvements in classification accuracy post-fine-tuning, validating the effectiveness of the PCT model for real-world ATR applications. The findings highlight the potential of transformer-based models in enhancing target recognition systems for future ATR systems based on 3-D radar images.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"180-192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105962","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

Recognition of Micromotion Space Targets at Low SNR Based on Complex-Valued Time Convolutional Attention Denoising Recognition Network 基于复值时间卷积注意去噪识别网络的低信噪比微动空间目标识别

IEEE Transactions on Radar Systems Pub Date : 2025-01-08 DOI: 10.1109/TRS.2025.3527209

Xueru Bai;Xuchen Mao;Xudong Tian;Feng Zhou

{"title":"Recognition of Micromotion Space Targets at Low SNR Based on Complex-Valued Time Convolutional Attention Denoising Recognition Network","authors":"Xueru Bai;Xuchen Mao;Xudong Tian;Feng Zhou","doi":"10.1109/TRS.2025.3527209","DOIUrl":"https://doi.org/10.1109/TRS.2025.3527209","url":null,"abstract":"For a micromotion space target, its narrowband radar cross section (RCS) series reflects the characteristics of target shape and motion. In practical scenarios, however, the RCS series of distant targets with weak scattering coefficients suffers from low signal-to-noise ratio (SNR), and performing separate noise suppression and recognition purely on the amplitude results in degraded recognition performance. To tackle this issue, an end-to-end complex-valued (CV) time convolutional attention denoising recognition network, dubbed as CV-TCANet, is proposed. Specifically, the denoising module captures temporal correlation by the CV attention mechanism and calculates the noise mask for denoising; and the recognition module utilizes the CV temporal convolutional network (CV-TCN) for feature extraction and recognition. In addition, a hybrid loss is designed to realize the integration of denoising and recognition, thus preserving target information while denoising and improving the recognition accuracy. Experimental results have proved that the proposed method could achieve satisfying recognition performance at low SNR.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"193-202"},"PeriodicalIF":0.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105961","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

Eliminating Range Migration Error in mm-Wave Radars for Angle of Arrival Estimation 毫米波雷达到达角估计中距离偏移误差的消除

IEEE Transactions on Radar Systems Pub Date : 2024-12-31 DOI: 10.1109/TRS.2024.3524574

Ferhat Can Ataman;Chethan Y. B. Kumar;Sandeep Rao;Sule Ozev

{"title":"Eliminating Range Migration Error in mm-Wave Radars for Angle of Arrival Estimation","authors":"Ferhat Can Ataman;Chethan Y. B. Kumar;Sandeep Rao;Sule Ozev","doi":"10.1109/TRS.2024.3524574","DOIUrl":"https://doi.org/10.1109/TRS.2024.3524574","url":null,"abstract":"Millimeter-wave (mm-Wave) radars are used to determine an object’s position relative to the radar, based on parameters such as range (R), azimuth angle (<inline-formula> <tex-math>$theta $ </tex-math></inline-formula>), and elevation angle (<inline-formula> <tex-math>$phi $ </tex-math></inline-formula>). Radars typically operate by transmitting a chirp signal, receiving the reflected signal from objects in the environment, and combining these signals at the receiver (RX). In systems with multiple antennas, the range is calculated for each transmitter (TX)–RX pair, producing multiple measurements that are averaged to improve accuracy. Angle estimation, however, relies on analyzing phase differences between antenna paths, and since it involves a single calculation across all antenna components, it does not benefit from averaging. In addition to random errors, systematic errors also affect the angle estimation. Specifically, the object’s distance varies slightly across the virtual antennas (formed by TX-RX combinations), causing shifts in the peak position of range estimation. This phenomenon, known as range migration, introduces errors. This article examines the root causes of range migration and its impact on angle of arrival (AoA) estimation, proposing effective solutions to mitigate these effects and enhance the overall accuracy of angle estimation.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"169-179"},"PeriodicalIF":0.0,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993317","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

A Planar Millimeter-Wave Diffuse-Reflection Suppression 4-D Imaging Radar Using L-Shaped Switchable Linearly Phased Array 基于l型可切换线性相控阵的平面毫米波漫反射抑制四维成像雷达

IEEE Transactions on Radar Systems Pub Date : 2024-12-27 DOI: 10.1109/TRS.2024.3523589

Huimin Liu;Jiawang Li;Zhang-Cheng Hao;Yun Hu;Gang Xu;Wei Hong

{"title":"A Planar Millimeter-Wave Diffuse-Reflection Suppression 4-D Imaging Radar Using L-Shaped Switchable Linearly Phased Array","authors":"Huimin Liu;Jiawang Li;Zhang-Cheng Hao;Yun Hu;Gang Xu;Wei Hong","doi":"10.1109/TRS.2024.3523589","DOIUrl":"https://doi.org/10.1109/TRS.2024.3523589","url":null,"abstract":"This article proposes a scatter suppression L-shaped phased-array imaging radar. The system operates at 24–26.4 GHz and is capable of 4-D imaging to determine the distance, elevation, azimuth, and speed of targets. It utilizes a frequency-modulated continuous-wave (FMCW) signal with a bandwidth of 2.4 GHz to extract range information, resulting in a range resolution of 62.5 mm. Orthogonal L-shaped linearly phased arrays are used for both transmission and reception. The azimuth and elevation angle information are obtained by switching the radiation beams of the phased arrays. The radar exhibits good scanning capabilities in 2-D space, with a scanning field of view (FOV) over 100° and an angular resolution of 3°. Importantly, the imaging artifacts due to multiple diffuse reflections can be suppressed by switching the transmit and receive phased-array antennas. A prototype is manufactured using the printed circuit board technology, which has a compact size of <inline-formula> <tex-math>$23.5times 23.5$ </tex-math></inline-formula> cm2. Experimental validation of the design has been conducted. The proposed radar architecture and array layout reduce the complexity of the baseband, offering advantages such as easy implementation, high integration, and low cost, showing promising prospects for potential sensing applications.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"155-168"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993306","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

Wave Height Estimation From Radar Images Under Rainy Conditions Based on Context-Aware Segmentation and Iterative Dehazing 基于上下文感知分割和迭代去雾的雨天雷达图像波高估计

IEEE Transactions on Radar Systems Pub Date : 2024-12-23 DOI: 10.1109/TRS.2024.3521814

Zhiding Yang;Weimin Huang

{"title":"Wave Height Estimation From Radar Images Under Rainy Conditions Based on Context-Aware Segmentation and Iterative Dehazing","authors":"Zhiding Yang;Weimin Huang","doi":"10.1109/TRS.2024.3521814","DOIUrl":"https://doi.org/10.1109/TRS.2024.3521814","url":null,"abstract":"This study introduces a novel approach to mitigate the impact of rain on significant wave height (SWH) measurements using X-band marine radar. First, the proposed method uses a transformer-based segmentation model, SegFormer, to divide radar images into four distinct regions: clear wave signatures, rain-contaminated areas, low backscatter areas, and wind-dominated rain areas. Given that radar wave signatures in rain-contaminated regions are significantly blurred, this segmentation step identifies regions with clear wave signatures, ensuring subsequent analysis to be more accurate. Next, an iterative dehazing method, which adaptively enhances image clarity based on gradient standard deviation (GSD), is applied to achieve optimal dehazing effects. Finally, the segmented and dehazed polar radar images are transformed into the Cartesian coordinates, where subimages from valid regions are selected for SWH estimation using the SWHFormer model. The radar dataset used for test was collected from a shipborne Decca radar in a sea area 300 km from Halifax, Canada, in 2008. The SegFormer model demonstrates superior segmentation performance, with 1.3% improvement in accuracy compared with the SegNet-based method. Besides, the iterative dehazing method significantly reduces haze effects in heavily contaminated images, outperforming traditional one-time dehazing methods in both precision and robustness for SWH estimation. Results show that the combination of segmentation and iterative dehazing reduces the root mean square deviation (RMSD) of SWH estimation from 0.42 and 0.33 to 0.28 m, compared with the existing support vector regression (SVR)-based and convolutional gated recurrent unit (CGRU)-based methods, and improves the correlation coefficient (CC) to 0.96. These advancements underscore the potential of integrating segmentation and adaptive dehazing for enhanced radar-based ocean monitoring under challenging meteorological conditions.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"101-114"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918348","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

2024 Index IEEE Transactions on Radar Systems Vol. 2 雷达系统学报，第2卷

IEEE Transactions on Radar Systems Pub Date : 2024-12-20 DOI: 10.1109/TRS.2024.3520733

引用次数: 0

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