Pub Date : 2025-10-06DOI: 10.1109/TRS.2025.3618181
Boyuan Dong;Yingning Dong;Yuxiao Li;Weibo Deng
Existing sparsity-driven blind-free range extension method for frequency-modulated interrupted continuous-wave (FMICW) radar faces the issues of inaccurate reconstruction and sizeable computational burden. This article proposes an accuracy-improved and efficient blind-free range extension method for FMICW radar via nonuniform sampling and density-guided sparse reconstruction to address the issues mentioned above. By improving the design of nonuniform sampling sequence, the spectral anti-aliasing performance can be improved. Then, density features are used to initialize the sparse regularization parameters of each scatterer. By utilizing different sparse regularization parameters within an observation scene, the proposed density-guided sparse reconstruction method is able to suppress the nonstructured noise caused by nonuniform sampling while retaining the information of weak targets. Compared to the existing sparsity-driven blind-free range extension method for FMICW radar, the proposed method improves the reconstruction accuracy and reduces the computational burden by reducing the number of iterations. Simulations and experiments on measured FMICW radar data demonstrate the effectiveness of the proposed method.
{"title":"Blind-Free Range Extension of FMICW Radar for Improving Reconstruction Accuracy of Weak Targets","authors":"Boyuan Dong;Yingning Dong;Yuxiao Li;Weibo Deng","doi":"10.1109/TRS.2025.3618181","DOIUrl":"https://doi.org/10.1109/TRS.2025.3618181","url":null,"abstract":"Existing sparsity-driven blind-free range extension method for frequency-modulated interrupted continuous-wave (FMICW) radar faces the issues of inaccurate reconstruction and sizeable computational burden. This article proposes an accuracy-improved and efficient blind-free range extension method for FMICW radar via nonuniform sampling and density-guided sparse reconstruction to address the issues mentioned above. By improving the design of nonuniform sampling sequence, the spectral anti-aliasing performance can be improved. Then, density features are used to initialize the sparse regularization parameters of each scatterer. By utilizing different sparse regularization parameters within an observation scene, the proposed density-guided sparse reconstruction method is able to suppress the nonstructured noise caused by nonuniform sampling while retaining the information of weak targets. Compared to the existing sparsity-driven blind-free range extension method for FMICW radar, the proposed method improves the reconstruction accuracy and reduces the computational burden by reducing the number of iterations. Simulations and experiments on measured FMICW radar data demonstrate the effectiveness of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1421-1434"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510156","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 : 2025-09-26DOI: 10.1109/TRS.2025.3614589
Katsuhisa Kashiwagi;Koichi Ichige
Hand gesture (HG) recognition using radar has been explored not only for human-to-computer interfaces in home appliances but also for device control in automotive applications. In such scenarios, the stationary clutter from the ground (or pavement), vehicles, furniture, human bodies, and other obstacles in the radar’s field of view affects target detection and degrades the accuracy of HG classification. Such stationary clutter is usually removed using a high-pass filtering technique. However, since the relative velocity in radar is calculated from the difference in the range along the radial direction from the radar over multiple chirps, the velocity decreases when the human hand is located near the center position of the radar. When using the conventional filtering method based on simple high-pass filtering, the number of detected points on the HG trajectory decreases. We therefore propose a more intelligent filtering method that removes only stationary clutter while preserving the detected points on the HG trajectory. In this work, we demonstrate the effectiveness of the proposed method through simulations and measurements using 79-GHz band frequency-modulated continuous-wave (FMCW) multiple-input–multiple-output (MIMO) radar with three transmit (TX) antennas and four receive (RX) antennas by classifying 11 classes of HGs. Our findings show that the proposed method performs better than the conventional filtering method, with an accuracy improvement ranging from about 2%–9% across different scenarios in both simulation and measurement, even when the power of clutter is higher than that of the human hand. The classification performance of the 11 classes of HGs showed an accuracy of 95.6% when mixture datasets consisting of a small portion of measurement datasets and synthetic datasets were used with the proposed method.
{"title":"Selective Clutter Removal for FMCW-Radar-Based Hand Gesture Recognition System","authors":"Katsuhisa Kashiwagi;Koichi Ichige","doi":"10.1109/TRS.2025.3614589","DOIUrl":"https://doi.org/10.1109/TRS.2025.3614589","url":null,"abstract":"Hand gesture (HG) recognition using radar has been explored not only for human-to-computer interfaces in home appliances but also for device control in automotive applications. In such scenarios, the stationary clutter from the ground (or pavement), vehicles, furniture, human bodies, and other obstacles in the radar’s field of view affects target detection and degrades the accuracy of HG classification. Such stationary clutter is usually removed using a high-pass filtering technique. However, since the relative velocity in radar is calculated from the difference in the range along the radial direction from the radar over multiple chirps, the velocity decreases when the human hand is located near the center position of the radar. When using the conventional filtering method based on simple high-pass filtering, the number of detected points on the HG trajectory decreases. We therefore propose a more intelligent filtering method that removes only stationary clutter while preserving the detected points on the HG trajectory. In this work, we demonstrate the effectiveness of the proposed method through simulations and measurements using 79-GHz band frequency-modulated continuous-wave (FMCW) multiple-input–multiple-output (MIMO) radar with three transmit (TX) antennas and four receive (RX) antennas by classifying 11 classes of HGs. Our findings show that the proposed method performs better than the conventional filtering method, with an accuracy improvement ranging from about 2%–9% across different scenarios in both simulation and measurement, even when the power of clutter is higher than that of the human hand. The classification performance of the 11 classes of HGs showed an accuracy of 95.6% when mixture datasets consisting of a small portion of measurement datasets and synthetic datasets were used with the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1324-1336"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405280","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}
The Bernoulli track-before-detect (BTBD) filter with amplitude measurements has achieved significant success in maritime radar surveillance for joint target detection and tracking. However, its independence assumption across target units limits its ability to accumulate energy from the extension of weak Swerling I targets, leading to underestimations of target existence and unexpected target misses. This article aims to effectively accumulate energy from short-exposure extensions of Swerling I targets within the BTBD framework. We first analyze the inconsistency between the measurement model of the existing BTBD filter and the pulse-to-pulse correlation characteristics of Swerling I targets. Based on this, we derive the theoretical likelihood function of the correlated amplitude model and propose two approximation methods for computational simplification. The first method employs maximum likelihood estimation (MLE) to approximate the theoretical likelihood while retaining the core architecture of the original BTBD filter, allowing for a straightforward comparison with the original method. The second method adopts an intensity resampling strategy based on the particle filtering implementation of the BTBD filter. This strategy seamlessly integrates a standard Bayesian filter to maintain a recursive estimation of the mean intensity of the target, thus providing adaptations to the fluctuations of Swerling I targets between scans. Experimental results using both simulated and measured radar data demonstrate the superior performance of the proposed filters in terms of trajectory integrity, interruption reduction, and state estimation accuracy.
{"title":"Bernoulli Track-Before-Detect Filter With Pulse-to-Pulse Correlated Amplitude Model for Maritime Swerling I Targets","authors":"Zhen Wang;Shiqi Pei;Chang Chen;Jun Liu;Pin Li;Weidong Chen","doi":"10.1109/TRS.2025.3614964","DOIUrl":"https://doi.org/10.1109/TRS.2025.3614964","url":null,"abstract":"The Bernoulli track-before-detect (BTBD) filter with amplitude measurements has achieved significant success in maritime radar surveillance for joint target detection and tracking. However, its independence assumption across target units limits its ability to accumulate energy from the extension of weak Swerling I targets, leading to underestimations of target existence and unexpected target misses. This article aims to effectively accumulate energy from short-exposure extensions of Swerling I targets within the BTBD framework. We first analyze the inconsistency between the measurement model of the existing BTBD filter and the pulse-to-pulse correlation characteristics of Swerling I targets. Based on this, we derive the theoretical likelihood function of the correlated amplitude model and propose two approximation methods for computational simplification. The first method employs maximum likelihood estimation (MLE) to approximate the theoretical likelihood while retaining the core architecture of the original BTBD filter, allowing for a straightforward comparison with the original method. The second method adopts an intensity resampling strategy based on the particle filtering implementation of the BTBD filter. This strategy seamlessly integrates a standard Bayesian filter to maintain a recursive estimation of the mean intensity of the target, thus providing adaptations to the fluctuations of Swerling I targets between scans. Experimental results using both simulated and measured radar data demonstrate the superior performance of the proposed filters in terms of trajectory integrity, interruption reduction, and state estimation accuracy.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1309-1323"},"PeriodicalIF":0.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315367","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 : 2025-09-16DOI: 10.1109/TRS.2025.3601306
Francesco Fioranelli;Shobha Sundar Ram;Julien Le Kernec;Sevgi Gurbuz
{"title":"Foreword to the Special Section on AI Approaches for Radar Processing and Applications","authors":"Francesco Fioranelli;Shobha Sundar Ram;Julien Le Kernec;Sevgi Gurbuz","doi":"10.1109/TRS.2025.3601306","DOIUrl":"https://doi.org/10.1109/TRS.2025.3601306","url":null,"abstract":"","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1243-1256"},"PeriodicalIF":0.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073414","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 problem of radar-based tracking of groups of people moving together and counting their numbers in indoor environments is considered here. A novel processing pipeline to track groups of people moving together and count their numbers is proposed and validated. The pipeline is specifically designed to deal with frequent changes of direction and stop-and-go movements typical of indoor activities. The proposed approach combines a tracker with a classifier to count the number of grouped people; this uses both spatial features extracted from range-azimuth (RA) maps and Doppler frequency features extracted with wavelet decomposition. Thus, the pipeline outputs over time both the location and the number of people present. The proposed approach is verified with experimental data collected with a 24-GHz frequency-modulated continuous-wave (FMCW) radar. It is shown that the proposed method achieves 93.15% accuracy in terms of counting the number of people and a tracking metric optimal subpattern assignment (OSPA) of 0.335. Furthermore, the performance is analyzed as a function of different relevant variables such as feature combinations and scenarios.
{"title":"Grouped Target Tracking and Seamless People Counting With a 24-GHz MIMO FMCW","authors":"Dingyang Wang;Sen Yuan;Alexander Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2025.3609436","DOIUrl":"https://doi.org/10.1109/TRS.2025.3609436","url":null,"abstract":"The problem of radar-based tracking of groups of people moving together and counting their numbers in indoor environments is considered here. A novel processing pipeline to track groups of people moving together and count their numbers is proposed and validated. The pipeline is specifically designed to deal with frequent changes of direction and stop-and-go movements typical of indoor activities. The proposed approach combines a tracker with a classifier to count the number of grouped people; this uses both spatial features extracted from range-azimuth (RA) maps and Doppler frequency features extracted with wavelet decomposition. Thus, the pipeline outputs over time both the location and the number of people present. The proposed approach is verified with experimental data collected with a 24-GHz frequency-modulated continuous-wave (FMCW) radar. It is shown that the proposed method achieves 93.15% accuracy in terms of counting the number of people and a tracking metric optimal subpattern assignment (OSPA) of 0.335. Furthermore, the performance is analyzed as a function of different relevant variables such as feature combinations and scenarios.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1298-1308"},"PeriodicalIF":0.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141783","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 : 2025-09-10DOI: 10.1109/TRS.2025.3608521
Chuncheng Zhao;Lei Wang;Zhiwei Yue;Yimin Liu
The aircraft target recognition is critical for radar early warning systems. The modulation of radar returns caused by the rotating components on the aircraft is known as the jet engine modulation (JEM) or helicopter rotation modulation (HERM). It has been proved that the JEM/HERM signals contain rich target features and can be employed to identify the target. For example, the Doppler span of the JEM/HERM signal responds to the blade length of the target’s rotor. Traditional feature extraction algorithms are based on spectral analysis to extract the blade length. However, these methods are limited by the nature of the line spectrum and cannot achieve high accuracy spectral width estimates. In this article, we propose a multicarrier-frequency (MCF) observation-based method to estimate the blade length. Larger carrier frequencies result in larger Doppler spans of the JEM/HERM signal. Thus, the traditional spectral width estimation is transformed into a slope estimation problem. In addition, by scheduling the carrier frequency sequence of the transmit pulses, long integration time for the Doppler processing of pulses at the same carrier frequency can be obtained. The effectiveness of the proposed method is proved by both the electromagnetic simulation data and real data.
{"title":"Enhanced Rotor Blade Length Extraction Using Multicarrier-Frequency Radar Observations","authors":"Chuncheng Zhao;Lei Wang;Zhiwei Yue;Yimin Liu","doi":"10.1109/TRS.2025.3608521","DOIUrl":"https://doi.org/10.1109/TRS.2025.3608521","url":null,"abstract":"The aircraft target recognition is critical for radar early warning systems. The modulation of radar returns caused by the rotating components on the aircraft is known as the jet engine modulation (JEM) or helicopter rotation modulation (HERM). It has been proved that the JEM/HERM signals contain rich target features and can be employed to identify the target. For example, the Doppler span of the JEM/HERM signal responds to the blade length of the target’s rotor. Traditional feature extraction algorithms are based on spectral analysis to extract the blade length. However, these methods are limited by the nature of the line spectrum and cannot achieve high accuracy spectral width estimates. In this article, we propose a multicarrier-frequency (MCF) observation-based method to estimate the blade length. Larger carrier frequencies result in larger Doppler spans of the JEM/HERM signal. Thus, the traditional spectral width estimation is transformed into a slope estimation problem. In addition, by scheduling the carrier frequency sequence of the transmit pulses, long integration time for the Doppler processing of pulses at the same carrier frequency can be obtained. The effectiveness of the proposed method is proved by both the electromagnetic simulation data and real data.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1273-1286"},"PeriodicalIF":0.0,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100514","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 : 2025-09-05DOI: 10.1109/TRS.2025.3606756
Nadav Levanon
To broaden the set of available periodic continuous wave (CW) waveforms, a new candidate employing ternary symmetric frequency coding with values (−1, 0, +1) is proposed. Its periodic autocorrelation function (PACF) closely resembles that of the well-known almost perfect sequence (APS), a binary phase-coded waveform with values (−1, +1). Both waveform families exhibit real-valued PACFs with zero sidelobes (SLs), except for a single, negative SL at the midpoint of the period. The binary phase-coded APS family is well-established, with sequence lengths N generally being multiples of 4. A particularly convenient subfamily, defined by $N =2$ ($p +1$ ), where p is any odd prime power, can be readily constructed. A transformation method is presented for converting a given APS phase-coded sequence into its frequency-coded counterpart. While a key limitation of frequency coding is that the symmetric spacing of frequency components around 0 is rigidly tied to the code element duration, a significant advantage is that both the transmitted and reference waveforms are unimodular.
{"title":"Ternary Frequency-Coded CW Radar Waveform Achieving Almost Perfect Periodic Autocorrelation","authors":"Nadav Levanon","doi":"10.1109/TRS.2025.3606756","DOIUrl":"https://doi.org/10.1109/TRS.2025.3606756","url":null,"abstract":"To broaden the set of available periodic continuous wave (CW) waveforms, a new candidate employing ternary symmetric frequency coding with values (−1, 0, +1) is proposed. Its periodic autocorrelation function (PACF) closely resembles that of the well-known almost perfect sequence (APS), a binary phase-coded waveform with values (−1, +1). Both waveform families exhibit real-valued PACFs with zero sidelobes (SLs), except for a single, negative SL at the midpoint of the period. The binary phase-coded APS family is well-established, with sequence lengths <italic>N</i> generally being multiples of 4. A particularly convenient subfamily, defined by <inline-formula> <tex-math>$N =2$ </tex-math></inline-formula>(<inline-formula> <tex-math>$p +1$ </tex-math></inline-formula>), where <italic>p</i> is any odd prime power, can be readily constructed. A transformation method is presented for converting a given APS phase-coded sequence into its frequency-coded counterpart. While a key limitation of frequency coding is that the symmetric spacing of frequency components around 0 is rigidly tied to the code element duration, a significant advantage is that both the transmitted and reference waveforms are unimodular.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1269-1272"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100418","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 : 2025-09-04DOI: 10.1109/TRS.2025.3605951
Baptiste Sambon;François De Saint Moulin;Guillaume Thiran;Claude Oestges;Luc Vandendorpe
Traditional radar and integrated sensing and communication (ISAC) systems often approximate targets as point sources, a simplification that fails to capture the essential scattering characteristics for many applications. This article presents a novel electromagnetic (EM)-based framework to accurately model the near-field (NF) scattering response of extended targets, which is then applied to three canonical shapes: a flat rectangular plate, a sphere, and a cylinder. Mathematical expressions for the received signal are provided in each case. Based on this model, the influence of bandwidth, carrier frequency, and target distance on localization accuracy is analyzed, showing how higher bandwidths and carrier frequencies improve resolution. Additionally, the impact of target curvature on localization performance is studied. Results indicate that detection performance is slightly enhanced when considering curved objects. A comparative analysis between the extended and point-target models shows significant similarities when targets are small and curved. However, as the target size increases or becomes flatter, the point-target model introduces estimation errors owing to model mismatch. The impact of this model mismatch as a function of system parameters is analyzed, and the operational zones where the point abstraction remains valid and where it breaks down are identified. These findings provide theoretical support for experimental results based on point-target models in previous studies.
{"title":"Electromagnetic Modeling of Extended Targets in a Distributed Antenna System","authors":"Baptiste Sambon;François De Saint Moulin;Guillaume Thiran;Claude Oestges;Luc Vandendorpe","doi":"10.1109/TRS.2025.3605951","DOIUrl":"https://doi.org/10.1109/TRS.2025.3605951","url":null,"abstract":"Traditional radar and integrated sensing and communication (ISAC) systems often approximate targets as point sources, a simplification that fails to capture the essential scattering characteristics for many applications. This article presents a novel electromagnetic (EM)-based framework to accurately model the near-field (NF) scattering response of extended targets, which is then applied to three canonical shapes: a flat rectangular plate, a sphere, and a cylinder. Mathematical expressions for the received signal are provided in each case. Based on this model, the influence of bandwidth, carrier frequency, and target distance on localization accuracy is analyzed, showing how higher bandwidths and carrier frequencies improve resolution. Additionally, the impact of target curvature on localization performance is studied. Results indicate that detection performance is slightly enhanced when considering curved objects. A comparative analysis between the extended and point-target models shows significant similarities when targets are small and curved. However, as the target size increases or becomes flatter, the point-target model introduces estimation errors owing to model mismatch. The impact of this model mismatch as a function of system parameters is analyzed, and the operational zones where the point abstraction remains valid and where it breaks down are identified. These findings provide theoretical support for experimental results based on point-target models in previous studies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1257-1268"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073413","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 : 2025-09-04DOI: 10.1109/TRS.2025.3606111
Marcel Follmann;Mohammad Alaee-Kerahroodi;Bhavani Shankar Mysore R;Volker Lücken;Andreas R. Diewald
We present a generalized framework for designing rectangular envelope (RE) window-based nonlinear frequency-modulated (NLFM) radar waveforms and derive a closed-form expression of the Cramér–Rao lower bound (CRLB) for their range-Doppler accuracy. This provides a means for creating accurate measurement covariance matrices needed in Kalman filter applications when using said waveform and may be utilized for waveform agile radar systems. First, the proposed approach shapes the waveforms power spectral density with a generalized cosine-sum window. Then, we construct the time domain representation using the principle of stationary phase (PoSP) and a Fourier series approximation. Next, the sidelobe behavior of the resulting waveform is analyzed and a method for masking mitigation by placing nulls as desired in the autocorrelation response is presented. Finally, we derive a closed-form expression for its CRLB and simplify it further for cases where only a reduced set of spectral shaping parameters is needed. Numerical simulations confirm that the theoretical findings match with Monte Carlo evaluations. The proposed waveform is more accurate than traditional ones based on Gaussian amplitude modulation (AM) when jointly estimating range and Doppler.
{"title":"Accuracy Estimation of Window-Based Nonlinear Frequency-Modulated Waveforms","authors":"Marcel Follmann;Mohammad Alaee-Kerahroodi;Bhavani Shankar Mysore R;Volker Lücken;Andreas R. Diewald","doi":"10.1109/TRS.2025.3606111","DOIUrl":"https://doi.org/10.1109/TRS.2025.3606111","url":null,"abstract":"We present a generalized framework for designing rectangular envelope (RE) window-based nonlinear frequency-modulated (NLFM) radar waveforms and derive a closed-form expression of the Cramér–Rao lower bound (CRLB) for their range-Doppler accuracy. This provides a means for creating accurate measurement covariance matrices needed in Kalman filter applications when using said waveform and may be utilized for waveform agile radar systems. First, the proposed approach shapes the waveforms power spectral density with a generalized cosine-sum window. Then, we construct the time domain representation using the principle of stationary phase (PoSP) and a Fourier series approximation. Next, the sidelobe behavior of the resulting waveform is analyzed and a method for masking mitigation by placing nulls as desired in the autocorrelation response is presented. Finally, we derive a closed-form expression for its CRLB and simplify it further for cases where only a reduced set of spectral shaping parameters is needed. Numerical simulations confirm that the theoretical findings match with Monte Carlo evaluations. The proposed waveform is more accurate than traditional ones based on Gaussian amplitude modulation (AM) when jointly estimating range and Doppler.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1287-1297"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100463","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 : 2025-09-02DOI: 10.1109/TRS.2025.3605232
Xikang Jiang;Jiahang Guo;Chong Rao;Lin Zhang;Lei Li
Ultrawideband (UWB) radar-based people counting (RPC) using deep learning-based methods has become a crucial technology for spatial awareness and monitoring in Internet of Things (IoT) applications. Recent deep learning approaches, particularly those combining convolutional neural networks (CNNs) with time-series processing modules such as transformers, have shown promise in RPC tasks. However, these modules were originally designed for applications like natural language processing (NLP). When directly applied to RPC, they may suffer from overfitting and low robustness due to the short-term and local correlation of radar signals. To address these challenges, an ultrawideband radar-based people counting method via time–frequency attention neural network is proposed, namely, UP-TIFA. UP-TIFA employs a dual-channel backbone network incorporating both time-domain and frequency-domain processing, enhancing spatial and temporal feature extraction. A time–frequency hybrid attention (TFHA) module is proposed, which integrates local attention mechanisms in both domains. A local sliding window restricts attention to spatially and temporally relevant regions, while a learnable gating mechanism adaptively fuses time and frequency domain outputs. To further improve model efficiency and generalization, a multihead orthogonal constraint (MOC) is introduced to enforce orthogonality among query and key projection matrices across different attention heads, reducing parameter redundancy. To handle with clutter from environmental noise and variations in electromagnetic wave attenuation due to distance and body orientation, an amplitude-phase joint optimization-based processing method is proposed, which enhances the signal-to-noise ratio (SNR) and stabilizes signal intensity across varying distances. A comprehensive radar dataset is collected in both open-hall and crowded indoor conference room environments for evaluation, featuring dynamic population counts ranging from 0 to 10 individuals in real-world conditions. Experimental results demonstrate that UP-TIFA achieves an average counting accuracy of 94.88%, outperforming the current state-of-the-art by 24.61%. Both the source code and the dataset are publicly available to facilitate further research.
{"title":"UP-TIFA: UWB Radar-Based People Counting via Time–Frequency Attention Neural Network","authors":"Xikang Jiang;Jiahang Guo;Chong Rao;Lin Zhang;Lei Li","doi":"10.1109/TRS.2025.3605232","DOIUrl":"https://doi.org/10.1109/TRS.2025.3605232","url":null,"abstract":"Ultrawideband (UWB) radar-based people counting (RPC) using deep learning-based methods has become a crucial technology for spatial awareness and monitoring in Internet of Things (IoT) applications. Recent deep learning approaches, particularly those combining convolutional neural networks (CNNs) with time-series processing modules such as transformers, have shown promise in RPC tasks. However, these modules were originally designed for applications like natural language processing (NLP). When directly applied to RPC, they may suffer from overfitting and low robustness due to the short-term and local correlation of radar signals. To address these challenges, an ultrawideband radar-based people counting method via time–frequency attention neural network is proposed, namely, UP-TIFA. UP-TIFA employs a dual-channel backbone network incorporating both time-domain and frequency-domain processing, enhancing spatial and temporal feature extraction. A time–frequency hybrid attention (TFHA) module is proposed, which integrates local attention mechanisms in both domains. A local sliding window restricts attention to spatially and temporally relevant regions, while a learnable gating mechanism adaptively fuses time and frequency domain outputs. To further improve model efficiency and generalization, a multihead orthogonal constraint (MOC) is introduced to enforce orthogonality among query and key projection matrices across different attention heads, reducing parameter redundancy. To handle with clutter from environmental noise and variations in electromagnetic wave attenuation due to distance and body orientation, an amplitude-phase joint optimization-based processing method is proposed, which enhances the signal-to-noise ratio (SNR) and stabilizes signal intensity across varying distances. A comprehensive radar dataset is collected in both open-hall and crowded indoor conference room environments for evaluation, featuring dynamic population counts ranging from 0 to 10 individuals in real-world conditions. Experimental results demonstrate that UP-TIFA achieves an average counting accuracy of 94.88%, outperforming the current state-of-the-art by 24.61%. Both the source code and the dataset are publicly available to facilitate further research.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1233-1242"},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028006","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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