Pub Date : 2025-03-03DOI: 10.1109/TRS.2025.3547245
Marek Wypich;Radoslaw Maksymiuk;Tomasz P. Zielinski
In this article, the possibilities of using the signal of 5G cellular networks for passive radar are investigated. In contrast to the traditional approach, i.e., the passive coherent location (PCL), in which the cross-ambiguity function (CAF) between the transmitted and received signal is calculated, in the presented method, known from the automotive industry, the channel frequency response (CFR) is first estimated, and then, the channel impulse response (CIR) is computed and spectrally analyzed to obtain a range-velocity map. It is shown that CFR/CIR-based 5G radar, known as an orthogonal frequency-division multiplexing (OFDM)-based radar, outperforms CAF-based 5G radar in some aspects, e.g., ease of implementation and lower complexity, while maintaining comparable measurement accuracy. In this article, CFR/CIR is estimated using standard 5G channel state information reference signals (CSI-RSs) or some additional radar-on-demand (RoD) OFDM symbols that could be offered by mobile network operators as an extra paid service. Different time and frequency densities of RoD OFDM symbols are tested. The results are compared with the application of CAF and 5G positioning reference-like signals (PRSs). This article shows that even rare CSI-RS pilots can make a low-cost radar device from a 5G receiver. It is demonstrated that slightly irregular sampling of the CIR taps, caused by using cyclic prefixes of different lengths in 5G, does not lead to major velocity estimation errors.
{"title":"5G-Based Passive Radar Utilizing Channel Response Estimated via Reference Signals","authors":"Marek Wypich;Radoslaw Maksymiuk;Tomasz P. Zielinski","doi":"10.1109/TRS.2025.3547245","DOIUrl":"https://doi.org/10.1109/TRS.2025.3547245","url":null,"abstract":"In this article, the possibilities of using the signal of 5G cellular networks for passive radar are investigated. In contrast to the traditional approach, i.e., the passive coherent location (PCL), in which the cross-ambiguity function (CAF) between the transmitted and received signal is calculated, in the presented method, known from the automotive industry, the channel frequency response (CFR) is first estimated, and then, the channel impulse response (CIR) is computed and spectrally analyzed to obtain a range-velocity map. It is shown that CFR/CIR-based 5G radar, known as an orthogonal frequency-division multiplexing (OFDM)-based radar, outperforms CAF-based 5G radar in some aspects, e.g., ease of implementation and lower complexity, while maintaining comparable measurement accuracy. In this article, CFR/CIR is estimated using standard 5G channel state information reference signals (CSI-RSs) or some additional radar-on-demand (RoD) OFDM symbols that could be offered by mobile network operators as an extra paid service. Different time and frequency densities of RoD OFDM symbols are tested. The results are compared with the application of CAF and 5G positioning reference-like signals (PRSs). This article shows that even rare CSI-RS pilots can make a low-cost radar device from a 5G receiver. It is demonstrated that slightly irregular sampling of the CIR taps, caused by using cyclic prefixes of different lengths in 5G, does not lead to major velocity estimation errors.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"511-519"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645348","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 : 2025-02-26DOI: 10.1109/TRS.2025.3546213
Shuyu Zheng;Dongsheng Li;Qingwei Yang;Yingjian Zhao;Libing Jiang;Zhuang Wang
Space-based radars (SBRs) systems are able to provide an unobstructed field of view for space target detection and tracking. However, the large temperature dynamic range and poor heat dissipation performance of the SBR system cause severe thermal noise, leading to deficiency in distant or dim space target detection tasks. In essence, the challenges above can be categorized as typical low signal-to-noise ratio (SNR) problems, and the track before detect (TBD) processing scheme is applied to solve them in this article. Nevertheless, the typical TBD methods reckon without the following aspects and thus are not well compatible with space target surveillance tasks via the SBR system. First, the typical TBD methods discard the phase information of radar raw data in constructing the likelihood ratio. In addition, most existing work merely considers modeling the amplitude fluctuation as Swerling types, which is not accurate enough for space targets when compared with the log-normal distribution (LND) model. Moreover, orbital space targets follow the orbital dynamic principle while most existing TBD methods neglect this important information, which will cause space targets filtering estimation bias. To address the aforementioned problems, we propose a TBD method based on the complex-amplitude likelihood ratio (CLR) of the LND model and soft orbit-information constraint (OC). In this article, with the aim of acquiring a more accurate likelihood ratio, we first derive the closed mathematical form of the amplitude likelihood ratio (ALR) and the CLR of the LND model. Meanwhile, some approximations are proposed to alleviate the integral computation. Then, the proposed ALR and CLR of the LND model are utilized to be implemented into the TBD scheme. Finally, we design elegant soft OC strategies to modify the associated weights corresponding with birth particles in sequential Monte Carlo (SMC) implementation. Simulation results are provided to validate the effectiveness of the proposed soft OC-CLR-TBD method.
{"title":"A Log-Normal Complex-Amplitude Likelihood Ratio-Based TBD Method With Soft Orbit-Information Constraints for Tracking Space Targets With Space-Based Radar","authors":"Shuyu Zheng;Dongsheng Li;Qingwei Yang;Yingjian Zhao;Libing Jiang;Zhuang Wang","doi":"10.1109/TRS.2025.3546213","DOIUrl":"https://doi.org/10.1109/TRS.2025.3546213","url":null,"abstract":"Space-based radars (SBRs) systems are able to provide an unobstructed field of view for space target detection and tracking. However, the large temperature dynamic range and poor heat dissipation performance of the SBR system cause severe thermal noise, leading to deficiency in distant or dim space target detection tasks. In essence, the challenges above can be categorized as typical low signal-to-noise ratio (SNR) problems, and the track before detect (TBD) processing scheme is applied to solve them in this article. Nevertheless, the typical TBD methods reckon without the following aspects and thus are not well compatible with space target surveillance tasks via the SBR system. First, the typical TBD methods discard the phase information of radar raw data in constructing the likelihood ratio. In addition, most existing work merely considers modeling the amplitude fluctuation as Swerling types, which is not accurate enough for space targets when compared with the log-normal distribution (LND) model. Moreover, orbital space targets follow the orbital dynamic principle while most existing TBD methods neglect this important information, which will cause space targets filtering estimation bias. To address the aforementioned problems, we propose a TBD method based on the complex-amplitude likelihood ratio (CLR) of the LND model and soft orbit-information constraint (OC). In this article, with the aim of acquiring a more accurate likelihood ratio, we first derive the closed mathematical form of the amplitude likelihood ratio (ALR) and the CLR of the LND model. Meanwhile, some approximations are proposed to alleviate the integral computation. Then, the proposed ALR and CLR of the LND model are utilized to be implemented into the TBD scheme. Finally, we design elegant soft OC strategies to modify the associated weights corresponding with birth particles in sequential Monte Carlo (SMC) implementation. Simulation results are provided to validate the effectiveness of the proposed soft OC-CLR-TBD method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"467-482"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645155","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 : 2025-02-26DOI: 10.1109/TRS.2025.3546001
Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli
This article studies the problem of estimating the 2-D motion state of a moving vehicle (ego motion) using millimeter-wave (mmWave) automotive radar sensors. Unlike prior single-radar or synchronized radar systems, the proposed approach (named DeepEgo+) can achieve sensor fusion and estimate ego motion using an unsynchronized radar sensor network. To achieve this goal, DeepEgo+ combines two neural network (NN)-based components (i.e., Module A for motion estimation and Module B for sensor fusion) with a decentralized processing architecture using the late fusion technique. Specifically, each radar sensor in the network has a Module A that processes its output and computes an initial motion estimate, while Module B fuses the initial estimates from all radar sensors and outputs the final estimate. This novel architecture and fusion scheme not only eliminates the synchronization requirement but also provides robustness and scalability to the system. To benchmark its performance, DeepEgo+ has been tested using a challenging real-world radar dataset, RadarScenes. The results show that DeepEgo+ provides significant performance advantages over recent state-of-the-art approaches in terms of estimation accuracy, long-term stability, and robustness against high outlier ratios and sensor failures. Furthermore, the influence of vehicle nonzero acceleration on ego-motion estimation is identified for the first time, and DeepEgo+ demonstrates the feasibility of compensating for its effect and further improving the estimation accuracy.
{"title":"DeepEgo+: Unsynchronized Radar Sensor Fusion for Robust Vehicle Ego-Motion Estimation","authors":"Simin Zhu;Satish Ravindran;Lihui Chen;Alexander G. Yarovoy;Francesco Fioranelli","doi":"10.1109/TRS.2025.3546001","DOIUrl":"https://doi.org/10.1109/TRS.2025.3546001","url":null,"abstract":"This article studies the problem of estimating the 2-D motion state of a moving vehicle (ego motion) using millimeter-wave (mmWave) automotive radar sensors. Unlike prior single-radar or synchronized radar systems, the proposed approach (named DeepEgo+) can achieve sensor fusion and estimate ego motion using an unsynchronized radar sensor network. To achieve this goal, DeepEgo+ combines two neural network (NN)-based components (i.e., Module A for motion estimation and Module B for sensor fusion) with a decentralized processing architecture using the late fusion technique. Specifically, each radar sensor in the network has a Module A that processes its output and computes an initial motion estimate, while Module B fuses the initial estimates from all radar sensors and outputs the final estimate. This novel architecture and fusion scheme not only eliminates the synchronization requirement but also provides robustness and scalability to the system. To benchmark its performance, DeepEgo+ has been tested using a challenging real-world radar dataset, RadarScenes. The results show that DeepEgo+ provides significant performance advantages over recent state-of-the-art approaches in terms of estimation accuracy, long-term stability, and robustness against high outlier ratios and sensor failures. Furthermore, the influence of vehicle nonzero acceleration on ego-motion estimation is identified for the first time, and DeepEgo+ demonstrates the feasibility of compensating for its effect and further improving the estimation accuracy.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"483-497"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645338","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 : 2025-02-19DOI: 10.1109/TRS.2025.3543722
Jiaming Zhang;Guillermo Álvarez-Narciandi;María García-Fernández;Rahul Sharma;Jie Zhang;Philipp del Hougne;Muhammad Ali Babar Abbasi;Okan Yurduseven
Computational imaging (CI)-based systems have emerged as a viable alternative to address the challenges of high hardware complexity and slow data acquisition speed associated with conventional microwave imaging. However, CI-based systems are limited by a substantial computational burden during the scene reconstruction process. In particular, image reconstruction and target classification problems for CI systems are computationally complex tasks. To tackle this challenge, a generative deep learning model named ClassiGAN is proposed to jointly solve the image reconstruction and target classification tasks by only using the backscattered measured signals as input. In particular, an adaptive loss function is employed to effectively integrate the respective loss functions for the two tasks, thereby enhancing training efficiency. This adaptive loss function dynamically adjusts the weights of the losses associated with each task, facilitating a more effective integration of the differing loss functions. Notably, ClassiGAN significantly reduces the run time for image reconstruction tasks compared to conventional CI methods. Compared to other state-of-the-art methods, ClassiGAN not only achieves lower average normalized mean squared error (NMSE) and higher structural similarity (SSIM) but also provides a higher accuracy in recognizing imaging targets. Extensive experimental tests further validate ClassiGAN’s capability to simultaneously reconstruct and recognize the imaging target within practical settings. Hence, this shows that ClassiGAN can enhance the overall efficiency of CI-based systems at microwave frequencies by addressing challenges related to computational load during run time.
{"title":"ClassiGAN: Joint Image Reconstruction and Classification in Computational Microwave Imaging","authors":"Jiaming Zhang;Guillermo Álvarez-Narciandi;María García-Fernández;Rahul Sharma;Jie Zhang;Philipp del Hougne;Muhammad Ali Babar Abbasi;Okan Yurduseven","doi":"10.1109/TRS.2025.3543722","DOIUrl":"https://doi.org/10.1109/TRS.2025.3543722","url":null,"abstract":"Computational imaging (CI)-based systems have emerged as a viable alternative to address the challenges of high hardware complexity and slow data acquisition speed associated with conventional microwave imaging. However, CI-based systems are limited by a substantial computational burden during the scene reconstruction process. In particular, image reconstruction and target classification problems for CI systems are computationally complex tasks. To tackle this challenge, a generative deep learning model named ClassiGAN is proposed to jointly solve the image reconstruction and target classification tasks by only using the backscattered measured signals as input. In particular, an adaptive loss function is employed to effectively integrate the respective loss functions for the two tasks, thereby enhancing training efficiency. This adaptive loss function dynamically adjusts the weights of the losses associated with each task, facilitating a more effective integration of the differing loss functions. Notably, ClassiGAN significantly reduces the run time for image reconstruction tasks compared to conventional CI methods. Compared to other state-of-the-art methods, ClassiGAN not only achieves lower average normalized mean squared error (NMSE) and higher structural similarity (SSIM) but also provides a higher accuracy in recognizing imaging targets. Extensive experimental tests further validate ClassiGAN’s capability to simultaneously reconstruct and recognize the imaging target within practical settings. Hence, this shows that ClassiGAN can enhance the overall efficiency of CI-based systems at microwave frequencies by addressing challenges related to computational load during run time.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"441-452"},"PeriodicalIF":0.0,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553115","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 : 2025-02-17DOI: 10.1109/TRS.2025.3542699
Alex Batts;Brian Rigling
Interferometric synthetic aperture radar (SAR) utilizes the phase difference between two images formed from separate channels to extract information from the imaged scene. Dual-channel systems provide a compromise between multipass and multichannel setups in that greater coherence between the channels is achieved while still being physically realizable. However, dual-channel systems suffer from less stability in phase estimates due to the inability to undergo sufficient averaging to reduce thermal noise. Spectral estimation techniques have the ability to reduce these effects and provide stable, accurate intensity and phase estimates. This article presents a novel extension of a previously developed technique for height estimation to the Amplitude and Phase EStimation (APES) filter, and develops a novel technique using linear prediction filters. In addition, the three techniques are extended to along-track interferometric phase stabilization for moving target indication (MTI). Quantitative results show APES performs best with respect to bias and standard deviation. Along-track interferometry (ATI) and topographic interferograms are presented to visually demonstrate performance improvements.
{"title":"Dual-Channel Joint SAR-Interferometry via Superresolution Spectral Estimation","authors":"Alex Batts;Brian Rigling","doi":"10.1109/TRS.2025.3542699","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542699","url":null,"abstract":"Interferometric synthetic aperture radar (SAR) utilizes the phase difference between two images formed from separate channels to extract information from the imaged scene. Dual-channel systems provide a compromise between multipass and multichannel setups in that greater coherence between the channels is achieved while still being physically realizable. However, dual-channel systems suffer from less stability in phase estimates due to the inability to undergo sufficient averaging to reduce thermal noise. Spectral estimation techniques have the ability to reduce these effects and provide stable, accurate intensity and phase estimates. This article presents a novel extension of a previously developed technique for height estimation to the Amplitude and Phase EStimation (APES) filter, and develops a novel technique using linear prediction filters. In addition, the three techniques are extended to along-track interferometric phase stabilization for moving target indication (MTI). Quantitative results show APES performs best with respect to bias and standard deviation. Along-track interferometry (ATI) and topographic interferograms are presented to visually demonstrate performance improvements.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"406-416"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535468","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 : 2025-02-14DOI: 10.1109/TRS.2025.3542410
Hongping Zhou;Xiaomin Cai;Peng Peng;Zhongyi Guo
Accurate identification of jamming is the premise of effective work of radar anti-jamming systems. As the electromagnetic environment becomes increasingly complex, radar detection faces not only the issue of insufficient training samples but also the challenge of imbalanced jamming samples. To solve this problem, this article proposes a few-shot recognition method of radar active jamming guided by prototype features. In this method, a pyramid structure is used to construct feature maps at different levels to integrate low-level features and high-level semantic features, so as to retain the information of the time-frequency images of the jamming signal to the maximum extent. Meanwhile, a differentiation information attention module is introduced to capture the global and local information of the feature maps and enhance the signal perception ability of the model. Finally, we propose a prototype feature extraction and fusion module to learn the prototype features of various samples and fuse them with backbone features. In view of the uneven data of the training set, the imbalanced coefficient is proposed to improve the recognition accuracy of the few-shot jamming signal in a complex electromagnetic environment. The experimental results on the jamming simulation dataset show that the proposed model has good recognition accuracy and robustness, and can handle imbalanced jamming samples. When the jamming-to-noise ratio (JNR) exceeds 2 dB, the average recognition accuracy of jamming can reach 99%. In the case of low JNR and sample imbalance, the proposed structure can effectively identify multiple small classes of jamming.
{"title":"Prototype Features Driven High-Performance Few-Shot Radar Active Jamming Recognition","authors":"Hongping Zhou;Xiaomin Cai;Peng Peng;Zhongyi Guo","doi":"10.1109/TRS.2025.3542410","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542410","url":null,"abstract":"Accurate identification of jamming is the premise of effective work of radar anti-jamming systems. As the electromagnetic environment becomes increasingly complex, radar detection faces not only the issue of insufficient training samples but also the challenge of imbalanced jamming samples. To solve this problem, this article proposes a few-shot recognition method of radar active jamming guided by prototype features. In this method, a pyramid structure is used to construct feature maps at different levels to integrate low-level features and high-level semantic features, so as to retain the information of the time-frequency images of the jamming signal to the maximum extent. Meanwhile, a differentiation information attention module is introduced to capture the global and local information of the feature maps and enhance the signal perception ability of the model. Finally, we propose a prototype feature extraction and fusion module to learn the prototype features of various samples and fuse them with backbone features. In view of the uneven data of the training set, the imbalanced coefficient is proposed to improve the recognition accuracy of the few-shot jamming signal in a complex electromagnetic environment. The experimental results on the jamming simulation dataset show that the proposed model has good recognition accuracy and robustness, and can handle imbalanced jamming samples. When the jamming-to-noise ratio (JNR) exceeds 2 dB, the average recognition accuracy of jamming can reach 99%. In the case of low JNR and sample imbalance, the proposed structure can effectively identify multiple small classes of jamming.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"430-440"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553325","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 : 2025-02-14DOI: 10.1109/TRS.2025.3542283
Matthew R. Ziemann;Christopher A. Metzler
We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background—while still being effective at ranging and sensing. To do so, we use an unsupervised, adversarial learning framework; our generator network produces waveforms designed to confuse a critic network, which is optimized to differentiate generated waveforms from the background. To ensure our generated waveforms are still effective for sensing, we introduce and minimize an ambiguity function-based loss on the generated waveforms. We evaluate the performance of our method by comparing the single-pulse detectability of our generated waveforms with traditional LPD waveforms using a separately trained detection neural network. We find that our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics. Our framework also provides a mechanism to trade-off detectability and sensing performance.
{"title":"Adaptive LPD Radar Waveform Design With Generative Deep Learning","authors":"Matthew R. Ziemann;Christopher A. Metzler","doi":"10.1109/TRS.2025.3542283","DOIUrl":"https://doi.org/10.1109/TRS.2025.3542283","url":null,"abstract":"We propose a learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background—while still being effective at ranging and sensing. To do so, we use an unsupervised, adversarial learning framework; our generator network produces waveforms designed to confuse a critic network, which is optimized to differentiate generated waveforms from the background. To ensure our generated waveforms are still effective for sensing, we introduce and minimize an ambiguity function-based loss on the generated waveforms. We evaluate the performance of our method by comparing the single-pulse detectability of our generated waveforms with traditional LPD waveforms using a separately trained detection neural network. We find that our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics. Our framework also provides a mechanism to trade-off detectability and sensing performance.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"417-429"},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535467","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}
High-frequency surface wave radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this article designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional constant false alarm rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.
{"title":"Intelligent Target Detection Method for HFSWR Based on Dual-Scale Branch Fusion Network and Adaptive Threshold Control","authors":"Yuanzheng Ji;Aijun Liu;Shuai Shao;Changjun Yu;Xuekun Chen","doi":"10.1109/TRS.2025.3540016","DOIUrl":"https://doi.org/10.1109/TRS.2025.3540016","url":null,"abstract":"High-frequency surface wave radar (HFSWR) is a crucial tool for oceanic remote sensing and surveillance; however, radar target detection is challenged by the presence of background clutter and interference. In response, this article designs a novel dual-scale branch fusion network specifically for detecting target signals in the range-Doppler (RD) spectrum. The network effectively enhances the ability to distinguish between targets and clutter by combining large-scale environmental feature sensing with small-scale target signal structure analysis. Additionally, we propose a novel detection threshold adjustment mechanism based on the RD spectrum perception network. First, an initial detection threshold is calculated using the traditional constant false alarm rate (CFAR) method. Then, the output of the softmax layer in the RD spectrum perception network is used to adjust the threshold, improving the robustness and accuracy of the detection process. The RD spectrum perception network is trained jointly using data from the Automatic Identification System (AIS) associated with HFSWR and simulated target-embedded data. Multiple validations and analyses of the proposed detection method are conducted with these datasets. Experimental results demonstrate that the proposed method has good detection performance, outperforming several other existing methods.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"379-391"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143496503","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 : 2025-02-06DOI: 10.1109/TRS.2025.3539289
Xuanyu Peng;Yaokun Hu;Ting Liu;Ying Wu;Tatsunori Saito;Takeshi Toda
This study explores the application of a 77 GHz mm-wave frequency-modulated continuous wave (FMCW) radar system for human activity recognition (HAR). We propose a novel density-aware convex hull (DACH) algorithm specifically designed to address the challenge of point cloud sparsity, which is particularly evident when using few-channel radar systems. This algorithm combines a triple-view convolutional neural network (CNN) and long short-term memory (LSTM) models for classification. Unlike traditional methods that often overlook the impact of sparse point cloud data, our approach emphasizes the importance of maintaining robust and dense data for precise activity recognition. Our experiments, which involved classifying nine human activities—standing, sitting, squatting, lying on the floor, transitions between these postures, and walking—demonstrate the method’s effectiveness. By using a compact 3Tx4Rx few-channel radar, we achieve a balance among cost, size, and performance, making it suitable for practical applications like indoor health monitoring and elderly care. The proposed method achieves an average classification accuracy of approximately 99.63% across all four scenarios, marking a significant improvement over existing approaches, and shows promise for real-time applications in various fields.
{"title":"Stability-Enhanced Human Activity Recognition With a Compact Few-Channel mm-Wave FMCW Radar","authors":"Xuanyu Peng;Yaokun Hu;Ting Liu;Ying Wu;Tatsunori Saito;Takeshi Toda","doi":"10.1109/TRS.2025.3539289","DOIUrl":"https://doi.org/10.1109/TRS.2025.3539289","url":null,"abstract":"This study explores the application of a 77 GHz mm-wave frequency-modulated continuous wave (FMCW) radar system for human activity recognition (HAR). We propose a novel density-aware convex hull (DACH) algorithm specifically designed to address the challenge of point cloud sparsity, which is particularly evident when using few-channel radar systems. This algorithm combines a triple-view convolutional neural network (CNN) and long short-term memory (LSTM) models for classification. Unlike traditional methods that often overlook the impact of sparse point cloud data, our approach emphasizes the importance of maintaining robust and dense data for precise activity recognition. Our experiments, which involved classifying nine human activities—standing, sitting, squatting, lying on the floor, transitions between these postures, and walking—demonstrate the method’s effectiveness. By using a compact 3Tx4Rx few-channel radar, we achieve a balance among cost, size, and performance, making it suitable for practical applications like indoor health monitoring and elderly care. The proposed method achieves an average classification accuracy of approximately 99.63% across all four scenarios, marking a significant improvement over existing approaches, and shows promise for real-time applications in various fields.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"360-378"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430444","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}
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}
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