Presents corrections to the paper, Errata to “Engineering Constraints and Application Regimes of Quantum Radar”.
{"title":"Corrections to “Engineering Constraints and Application Regimes of Quantum Radar”","authors":"Florian Bischeltsrieder;Michael Würth;Johannes Russer;Markus Peichl;Wolfgang Utschick","doi":"10.1109/TRS.2025.3532053","DOIUrl":"https://doi.org/10.1109/TRS.2025.3532053","url":null,"abstract":"Presents corrections to the paper, Errata to “Engineering Constraints and Application Regimes of Quantum Radar”.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"246-246"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105940","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}
In the realm of modern radar electronic warfare, hostile jamming signals with time-variant polarization states pose a significant challenge to the performance of host radars. This article presents a signal-processing scheme specifically designed to suppress polarization-agile jamming signals in dual-polarized digital array radars (DARs). By innovatively modeling the polarization-agile jamming signal as two orthogonal linearly polarized signals sharing the same elevation-azimuth angle, a direction-cosine estimation and association algorithm tailored for such signals is derived. Furthermore, a spatial covariance matrix reconstruction (CMR) method that uniquely extracts the time-varying polarization parameters of each jamming signal is developed. Building upon this, a spatial-polarization CMR method is devised to effectively suppress all polarization-agile jamming signals. The key innovation lies in achieving adaptive polarization matching during the cancellation process, which sets this scheme apart from conventional radar signal-processing approaches. Simulation results underscore the superiority of the proposed scheme, demonstrating significant performance enhancements over commonly used methodologies.
{"title":"Polarization-Agile Jamming Suppression for Dual-Polarized Digital Array Radars","authors":"Zhigang Wang;Jin He;Ting Shu;Ning Zhang;Xiang Lu;Junfeng Wang;Trieu-Kien Truong","doi":"10.1109/TRS.2025.3530404","DOIUrl":"https://doi.org/10.1109/TRS.2025.3530404","url":null,"abstract":"In the realm of modern radar electronic warfare, hostile jamming signals with time-variant polarization states pose a significant challenge to the performance of host radars. This article presents a signal-processing scheme specifically designed to suppress polarization-agile jamming signals in dual-polarized digital array radars (DARs). By innovatively modeling the polarization-agile jamming signal as two orthogonal linearly polarized signals sharing the same elevation-azimuth angle, a direction-cosine estimation and association algorithm tailored for such signals is derived. Furthermore, a spatial covariance matrix reconstruction (CMR) method that uniquely extracts the time-varying polarization parameters of each jamming signal is developed. Building upon this, a spatial-polarization CMR method is devised to effectively suppress all polarization-agile jamming signals. The key innovation lies in achieving adaptive polarization matching during the cancellation process, which sets this scheme apart from conventional radar signal-processing approaches. Simulation results underscore the superiority of the proposed scheme, demonstrating significant performance enhancements over commonly used methodologies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"247-259"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105941","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}
Radar sensors are widely used to support further system automation as they reliably provide range, velocity, and angle information for multiple objects. Nevertheless, unique identification of an object is not within the typical function range of radar; often, supportive systems are required. To overcome this limitation, a multipurpose tag concept for fast chirp frequency-modulated continuous wave (FC-FMCW) radar is introduced. The Doppler tag applies an artificial frequency shift following the Doppler phenomenon for unique identification of tagged objects with conventional radar hardware. It enables simultaneous detection and feature estimation of tagged and untagged objects. The idea of the Doppler tag is presented together with a realization and the required signal processing to allow for identification and high-accuracy range estimation. Simulations and measurements are provided to support the overall understanding and prove the functionality of the radar-tag system.
{"title":"Identification and High-Accuracy Range Estimation With Doppler Tags in Radar Applications","authors":"Theresa Antes;Paul Schubert;Thomas Zwick;Benjamin Nuss","doi":"10.1109/TRS.2025.3530560","DOIUrl":"https://doi.org/10.1109/TRS.2025.3530560","url":null,"abstract":"Radar sensors are widely used to support further system automation as they reliably provide range, velocity, and angle information for multiple objects. Nevertheless, unique identification of an object is not within the typical function range of radar; often, supportive systems are required. To overcome this limitation, a multipurpose tag concept for fast chirp frequency-modulated continuous wave (FC-FMCW) radar is introduced. The Doppler tag applies an artificial frequency shift following the Doppler phenomenon for unique identification of tagged objects with conventional radar hardware. It enables simultaneous detection and feature estimation of tagged and untagged objects. The idea of the Doppler tag is presented together with a realization and the required signal processing to allow for identification and high-accuracy range estimation. Simulations and measurements are provided to support the overall understanding and prove the functionality of the radar-tag system.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"260-271"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105942","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}
Pub Date : 2025-01-13DOI: 10.1109/TRS.2025.3528773
Moritz Kahlert;Tai Fei;Claas Tebruegge;Markus Gardill
Digitally modulated radar systems, such as phase-modulated continuous-wave (PMCW), often struggle with high bandwidth demands for fine-range resolutions, posing challenges for cost-effective automotive applications. To address this issue, we propose an stepped-frequency PMCW (SF-PMCW) radar waveform in which the instantaneous bandwidth of a single pulse is extensively reduced while the range resolution is beyond the theoretical limit imposed by the instantaneous bandwidth. The proposed waveform spans a synthetic bandwidth across multiple pulses, achieving range estimates comparable to those typically achieved with higher instantaneous bandwidths. Simultaneously, the requirements for analog-to-digital converters (ADCs) are relaxed. Simulations have been performed to demonstrate the performance. The results indicate that the proposed SF-PMCW waveform with an instantaneous bandwidth of 100 MHz can achieve range estimates as good as a PMCW waveform with an instantaneous bandwidth of 1 GHz.
{"title":"Stepped-Frequency PMCW Waveforms for Automotive Radar Applications","authors":"Moritz Kahlert;Tai Fei;Claas Tebruegge;Markus Gardill","doi":"10.1109/TRS.2025.3528773","DOIUrl":"https://doi.org/10.1109/TRS.2025.3528773","url":null,"abstract":"Digitally modulated radar systems, such as phase-modulated continuous-wave (PMCW), often struggle with high bandwidth demands for fine-range resolutions, posing challenges for cost-effective automotive applications. To address this issue, we propose an stepped-frequency PMCW (SF-PMCW) radar waveform in which the instantaneous bandwidth of a single pulse is extensively reduced while the range resolution is beyond the theoretical limit imposed by the instantaneous bandwidth. The proposed waveform spans a synthetic bandwidth across multiple pulses, achieving range estimates comparable to those typically achieved with higher instantaneous bandwidths. Simultaneously, the requirements for analog-to-digital converters (ADCs) are relaxed. Simulations have been performed to demonstrate the performance. The results indicate that the proposed SF-PMCW waveform with an instantaneous bandwidth of 100 MHz can achieve range estimates as good as a PMCW waveform with an instantaneous bandwidth of 1 GHz.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"233-245"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105939","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}
Pub Date : 2025-01-10DOI: 10.1109/TRS.2025.3528032
Zeyu Wang;Hongmeng Chen;Shuwen Xu;Ming Li
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.
{"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}
Pub Date : 2025-01-09DOI: 10.1109/TRS.2025.3527884
Wietse Bouwmeester;Francesco Fioranelli;Alexander G. Yarovoy
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.
{"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}
Pub Date : 2025-01-09DOI: 10.1109/TRS.2025.3527882
David Schvartzman;Robert D. Palmer;Matthew Herndon;Mark B. Yeary
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 $0.1^{circ },$ , 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.
{"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}
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.
{"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}
Pub Date : 2025-01-08DOI: 10.1109/TRS.2025.3527209
Xueru Bai;Xuchen Mao;Xudong Tian;Feng Zhou
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.
{"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}
Pub Date : 2024-12-31DOI: 10.1109/TRS.2024.3524574
Ferhat Can Ataman;Chethan Y. B. Kumar;Sandeep Rao;Sule Ozev
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 ($theta $ ), and elevation angle ($phi $ ). 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.
{"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}
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