Pub Date : 2025-01-27DOI: 10.1109/TRS.2025.3534805
Yuxuan Zhang;Jianxin Wu;Lei Zhang;Mengya Li
Due to the unknown location of the noncooperative airborne emitter, their application as external radiation for passive radar is limited. Therefore, its location and velocity estimation is a significant problem. This article first proposes an airborne emitter location and velocity estimation method in airborne passive radar using angle-Doppler frequencies on the clutter spectrum peaks (SPs). First, the frequencies on clutter SPs in each range bin are estimated by the minimum variance distortionless response (MVDR) of clutter with sub-aperture smoothing (SASM) techniques. Second, a parametric approach is proposed to formulate the relationship between the frequencies and parameters of interest. Finally, an intermediate unknown is introduced to simplify the 6-D parameter optimization problem into a 1-D search. The advantages: The proposed method utilizes clutter information to enhance observability of single-station in a short period, and robustly estimate the location and velocity with a closed-form solution. Cramer-Rao lower bound (CRLB) is derived for theoretical analysis. Simulation experiments validate the effectiveness and advantages of the proposed method.
{"title":"Instantaneous Single-Station Location and Velocity Estimation of Airborne Noncooperative Emitter Using Clutter Angle-Doppler Frequencies","authors":"Yuxuan Zhang;Jianxin Wu;Lei Zhang;Mengya Li","doi":"10.1109/TRS.2025.3534805","DOIUrl":"https://doi.org/10.1109/TRS.2025.3534805","url":null,"abstract":"Due to the unknown location of the noncooperative airborne emitter, their application as external radiation for passive radar is limited. Therefore, its location and velocity estimation is a significant problem. This article first proposes an airborne emitter location and velocity estimation method in airborne passive radar using angle-Doppler frequencies on the clutter spectrum peaks (SPs). First, the frequencies on clutter SPs in each range bin are estimated by the minimum variance distortionless response (MVDR) of clutter with sub-aperture smoothing (SASM) techniques. Second, a parametric approach is proposed to formulate the relationship between the frequencies and parameters of interest. Finally, an intermediate unknown is introduced to simplify the 6-D parameter optimization problem into a 1-D search. The advantages: The proposed method utilizes clutter information to enhance observability of single-station in a short period, and robustly estimate the location and velocity with a closed-form solution. Cramer-Rao lower bound (CRLB) is derived for theoretical analysis. Simulation experiments validate the effectiveness and advantages of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"392-405"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521494","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 : 2025-01-23DOI: 10.1109/TRS.2025.3533496
Liang Zhang;Qinglei Du;Weijian Liu;Hui Chen;Yongliang Wang
A method based on data extrapolation is proposed to enhance the range-Doppler (RD) resolution of ground-based radar. The method employs both the bandwidth extrapolation (BWE), a well-known range super-resolution technique, and the time width extrapolation (TWE), an interesting extension of BWE from the fast-time frequency to slow time, to construct the radar returns with larger bandwidth and more pulses. This method consists of two key parts: first performing TWE on the echoes after pulse compression and then performing BWE on the integrated echoes, which can bring better performance because the gain in radar signal processing is fully utilized. Not only this, an improved data extrapolation technique is designed and applied to the proposed method. The improvement is the generalization of the existing linear prediction-based extrapolation method and can ensure that radar resolution is further enhanced if a parameter of the suppression factor is set reasonably. The experiments based on the simulated data and the observations of vehicles on highway by an S-band ground-based radar illustrate that radar resolution can be improved by a factor of at least 3 with almost undistorted RD images and clutter narrowed in Doppler using a conservative suppression factor setting of $0.5sim 1$ .
{"title":"Range–Doppler Resolution Enhancement of Ground-Based Radar by Data Extrapolation Technique","authors":"Liang Zhang;Qinglei Du;Weijian Liu;Hui Chen;Yongliang Wang","doi":"10.1109/TRS.2025.3533496","DOIUrl":"https://doi.org/10.1109/TRS.2025.3533496","url":null,"abstract":"A method based on data extrapolation is proposed to enhance the range-Doppler (RD) resolution of ground-based radar. The method employs both the bandwidth extrapolation (BWE), a well-known range super-resolution technique, and the time width extrapolation (TWE), an interesting extension of BWE from the fast-time frequency to slow time, to construct the radar returns with larger bandwidth and more pulses. This method consists of two key parts: first performing TWE on the echoes after pulse compression and then performing BWE on the integrated echoes, which can bring better performance because the gain in radar signal processing is fully utilized. Not only this, an improved data extrapolation technique is designed and applied to the proposed method. The improvement is the generalization of the existing linear prediction-based extrapolation method and can ensure that radar resolution is further enhanced if a parameter of the suppression factor is set reasonably. The experiments based on the simulated data and the observations of vehicles on highway by an S-band ground-based radar illustrate that radar resolution can be improved by a factor of at least 3 with almost undistorted RD images and clutter narrowed in Doppler using a conservative suppression factor setting of <inline-formula> <tex-math>$0.5sim 1$ </tex-math></inline-formula>.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"290-302"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360948","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 : 2025-01-22DOI: 10.1109/TRS.2025.3532831
Samuel P. Lavery;Tharmalingam Ratnarajah
Increased demands from communications and remote sensing systems on a limited radio frequency (RF) spectrum motivate design of dual-function radar communications (DFRC) systems. Simultaneously performing dual functions from one system circumvents cross-system interference. Orthogonal frequency-division multiplexing (OFDM) waveforms are common in telecommunications, and echoes from targets can be compared with a transmitted signal to isolate phase shifts related to targets’ ranges, velocities, azimuth angles, and elevation angles. By extending the modified matrix enhancement matrix pencil (MMEMP) technique to four dimensions, and compensating for intercarrier interference (ICI), this article presents a method of estimating the phase shifts and thereby the target parameters. The Cramér-Rao lower bound (CRLB) is derived, and differently parameterized fifth-generation new radio (5G NR)-inspired systems are simulated, demonstrating superior precision to Fourier- and multiple signal classification (MUSIC)-based parameter estimation with a much smaller time-frequency resource block.
{"title":"OFDM-Based Remote Sensing Using the 4-D Modified Matrix Pencil Method","authors":"Samuel P. Lavery;Tharmalingam Ratnarajah","doi":"10.1109/TRS.2025.3532831","DOIUrl":"https://doi.org/10.1109/TRS.2025.3532831","url":null,"abstract":"Increased demands from communications and remote sensing systems on a limited radio frequency (RF) spectrum motivate design of dual-function radar communications (DFRC) systems. Simultaneously performing dual functions from one system circumvents cross-system interference. Orthogonal frequency-division multiplexing (OFDM) waveforms are common in telecommunications, and echoes from targets can be compared with a transmitted signal to isolate phase shifts related to targets’ ranges, velocities, azimuth angles, and elevation angles. By extending the modified matrix enhancement matrix pencil (MMEMP) technique to four dimensions, and compensating for intercarrier interference (ICI), this article presents a method of estimating the phase shifts and thereby the target parameters. The Cramér-Rao lower bound (CRLB) is derived, and differently parameterized fifth-generation new radio (5G NR)-inspired systems are simulated, demonstrating superior precision to Fourier- and multiple signal classification (MUSIC)-based parameter estimation with a much smaller time-frequency resource block.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"318-331"},"PeriodicalIF":0.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422869","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}
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 : 2025-01-14DOI: 10.1109/TRS.2025.3529760
Fraser K. Coutts;John Thompson;Bernard Mulgrew
The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to maximize efficiency when sharing positioning, navigation, and timing (PNT) information captured by distributed networks of low size, weight, power, and cost (SWAP-C) RF sensors when operating in congested or contested electromagnetic environments (EMEs). By implementing effective PNT information-sharing strategies, these networks can more easily position the sensors or characterize targets of interest. In this work, we propose a novel ML-inspired compression design framework that improves efficiency when sharing PNT information in a network of sensors receiving radar waveforms. In addition, through novel learning procedures, the network can adapt to unforeseen EMEs such that network efficiency can be maintained in the presence of unforeseen RF waveforms and sensor surroundings. We show that our intelligent, model-driven, ML-inspired data reduction strategies can outperform alternative strategies that do not best-utilize the information content of waveforms in the EME. In addition, we demonstrate the ability of our strategies to adapt to changing mission goals by balancing different types of PNT information and learning from developing EMEs.
{"title":"Flexible Compression for Efficient Information Sharing in a Network of Radio Frequency Sensors","authors":"Fraser K. Coutts;John Thompson;Bernard Mulgrew","doi":"10.1109/TRS.2025.3529760","DOIUrl":"https://doi.org/10.1109/TRS.2025.3529760","url":null,"abstract":"The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to maximize efficiency when sharing positioning, navigation, and timing (PNT) information captured by distributed networks of low size, weight, power, and cost (SWAP-C) RF sensors when operating in congested or contested electromagnetic environments (EMEs). By implementing effective PNT information-sharing strategies, these networks can more easily position the sensors or characterize targets of interest. In this work, we propose a novel ML-inspired compression design framework that improves efficiency when sharing PNT information in a network of sensors receiving radar waveforms. In addition, through novel learning procedures, the network can adapt to unforeseen EMEs such that network efficiency can be maintained in the presence of unforeseen RF waveforms and sensor surroundings. We show that our intelligent, model-driven, ML-inspired data reduction strategies can outperform alternative strategies that do not best-utilize the information content of waveforms in the EME. In addition, we demonstrate the ability of our strategies to adapt to changing mission goals by balancing different types of PNT information and learning from developing EMEs.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"332-348"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422868","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}
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