Radar systems are used in safety-critical applications in vehicles, so it is necessary to ensure their functioning is reliable and trustworthy. System-on-chip (SoC) radars, which are commonly used now-a-days, are inherently vulnerable to data manipulation and attacks to gain intellectual property (IP) of the system. This article outlines the vulnerabilities of the SoC radars and proposes a distributed signal processing to improve the resilience of the system. The trustworthiness of the system is improved by partitioning the signal processing into smaller modules. We propose to implement these modules on separate processors such that it is made up of multiple application-specific integrated circuits (ASICs). Furthermore, a sparse antenna topology is proposed to limit the information stored in these modules. Therefore, it is difficult to execute a successful attack or gain any knowledge of the targets or system design based on the compromised data in one ASIC. This article introduces the generic structure for partitioning the signal processing steps involved in target detection and the sparse array topology used by the 77-GHz radar. A method for estimating the azimuth and elevation angles for the considered sparse array is also introduced.
{"title":"Signal Processing Architecture for a Trustworthy 77-GHz MIMO Radar","authors":"Ram Kishore Arumugam;André Froehly;Patrick Wallrath;Reinhold Herschel;Nils Pohl","doi":"10.1109/TRS.2024.3479711","DOIUrl":"https://doi.org/10.1109/TRS.2024.3479711","url":null,"abstract":"Radar systems are used in safety-critical applications in vehicles, so it is necessary to ensure their functioning is reliable and trustworthy. System-on-chip (SoC) radars, which are commonly used now-a-days, are inherently vulnerable to data manipulation and attacks to gain intellectual property (IP) of the system. This article outlines the vulnerabilities of the SoC radars and proposes a distributed signal processing to improve the resilience of the system. The trustworthiness of the system is improved by partitioning the signal processing into smaller modules. We propose to implement these modules on separate processors such that it is made up of multiple application-specific integrated circuits (ASICs). Furthermore, a sparse antenna topology is proposed to limit the information stored in these modules. Therefore, it is difficult to execute a successful attack or gain any knowledge of the targets or system design based on the compromised data in one ASIC. This article introduces the generic structure for partitioning the signal processing steps involved in target detection and the sparse array topology used by the 77-GHz radar. A method for estimating the azimuth and elevation angles for the considered sparse array is also introduced.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1112-1122"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595791","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}
When using a distributed multiple-input multiple-output (MIMO) radar system, one must account for nonideal and unknown effects due to the electronics, cables, antennas, and so on. This article addresses the problem of estimating the MIMO system transfer function coefficients of a linear time-invariant (LTI) MIMO system. The system is considered to be uncalibrated in that its MIMO transfer function, receiver noise powers, and noise spatial correlations are unknown. The problem of estimating the MIMO system transfer function coefficients is shown to be nontrivial due to its inherent Kronecker structure and is shown to be of the form of a class of unsolved problems. Three approaches for estimating the transfer function are derived and shown to achieve good performance in simulation. The first approach relaxes the constraints and finds the corresponding (relaxed) maximum likelihood estimator (MLE). The second approach projects the relaxed MLE solution into the constraint (Kronecker) set. The third approach makes use of the fact that the original transfer function MLE problem is biconvex in the transmit and receive transfer functions, respectively, and employs an alternating minimization algorithm to find them directly.
{"title":"Calibration of Distributed MIMO Radar Systems","authors":"Christine Bryant;Lee Patton;Brian Rigling;Braham Himed","doi":"10.1109/TRS.2024.3479070","DOIUrl":"https://doi.org/10.1109/TRS.2024.3479070","url":null,"abstract":"When using a distributed multiple-input multiple-output (MIMO) radar system, one must account for nonideal and unknown effects due to the electronics, cables, antennas, and so on. This article addresses the problem of estimating the MIMO system transfer function coefficients of a linear time-invariant (LTI) MIMO system. The system is considered to be uncalibrated in that its MIMO transfer function, receiver noise powers, and noise spatial correlations are unknown. The problem of estimating the MIMO system transfer function coefficients is shown to be nontrivial due to its inherent Kronecker structure and is shown to be of the form of a class of unsolved problems. Three approaches for estimating the transfer function are derived and shown to achieve good performance in simulation. The first approach relaxes the constraints and finds the corresponding (relaxed) maximum likelihood estimator (MLE). The second approach projects the relaxed MLE solution into the constraint (Kronecker) set. The third approach makes use of the fact that the original transfer function MLE problem is biconvex in the transmit and receive transfer functions, respectively, and employs an alternating minimization algorithm to find them directly.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"124-134"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1109/TRS.2024.3477353
Mate Toth;Erik Leitinger;Klaus Witrisal
Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous-wave (FMCW) radar. In this article, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge since both the coherent radar echo and the noncoherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present.
{"title":"Variational Signal Separation for Automotive Radar Interference Mitigation","authors":"Mate Toth;Erik Leitinger;Klaus Witrisal","doi":"10.1109/TRS.2024.3477353","DOIUrl":"https://doi.org/10.1109/TRS.2024.3477353","url":null,"abstract":"Algorithms for mutual interference mitigation and object parameter estimation are a key enabler for automotive applications of frequency-modulated continuous-wave (FMCW) radar. In this article, we introduce a signal separation method to detect and estimate radar object parameters while jointly estimating and successively canceling the interference signal. The underlying signal model poses a challenge since both the coherent radar echo and the noncoherent interference influenced by individual multipath propagation channels must be considered. Under certain assumptions, the model is described as a superposition of multipath channels weighted by parametric interference chirp envelopes. Inspired by sparse Bayesian learning (SBL), we employ an augmented probabilistic model that uses a hierarchical gamma-Gaussian prior model for each multipath channel. Based on this, an iterative inference algorithm is derived using the variational expectation-maximization (EM) methodology. The algorithm is statistically evaluated in terms of object parameter estimation accuracy and robustness, indicating that it is fundamentally capable of achieving the Cramer-Rao lower bound (CRLB) with respect to the accuracy of object estimates and it closely follows the radar performance achieved when no interference is present.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1007-1026"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10711887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524214","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 : 2024-10-08DOI: 10.1109/TRS.2024.3476411
Mahshid Asri;Rahul Chowdhury;Allison Care;David Femi Lamptey;Ann Morgenthaler;Octavia Camps;Carey M. Rappaport
Automatic detection and localization of anomalies on radar images of personnel taken at the airport security checkpoints is a necessary step of having an end-to-end automatic threat detection algorithm. This article presents two deep learning-based solutions for pixel-wise localization of body-worn anomalies. The trained 2-D and semi-supervised U-Net models can accurately detect and localize foreign objects on all body regions by producing anomaly and body masks for each input radar image.
{"title":"Pixel-Wise Localization of Concealed Objects on Millimeter-Wave Radar Images Using Deep Learning","authors":"Mahshid Asri;Rahul Chowdhury;Allison Care;David Femi Lamptey;Ann Morgenthaler;Octavia Camps;Carey M. Rappaport","doi":"10.1109/TRS.2024.3476411","DOIUrl":"https://doi.org/10.1109/TRS.2024.3476411","url":null,"abstract":"Automatic detection and localization of anomalies on radar images of personnel taken at the airport security checkpoints is a necessary step of having an end-to-end automatic threat detection algorithm. This article presents two deep learning-based solutions for pixel-wise localization of body-worn anomalies. The trained 2-D and semi-supervised U-Net models can accurately detect and localize foreign objects on all body regions by producing anomaly and body masks for each input radar image.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1027-1035"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1109/TRS.2024.3472075
Jiangnan Zhong;Gangsheng Li;Ling Zhang;Lanjun Liu;Q. M. Jonathan Wu
Shipborne high-frequency surface wave radar (HFSWR) systems face the challenge of sea clutter spreading, which obscures vessel echoes and makes detection difficult. In this article, we propose a novel 3-D target detection algorithm that effectively identifies vessel targets in sea clutter using multidimensional fusion features. The algorithm consists of two stages: 3-D spectrum construction and target detection. In the 3-D spectrum construction stage, the digital narrow beam forming (DNBF) method is combined to transform the range-Doppler (RD) spectrum into a range-Doppler–azimuth 3-D spectrum. In the target detection stage, a two-level cascade target detection algorithm is proposed. At the first level, a 3-D extremum detection algorithm identifies potential vessels in sea clutter from the 3-D spectrum and locates the 3-D tensor blocks containing high-dimensional morphology features of these potential vessels. At the second level, we introduce an intelligent 3-D tensor block classifier, which includes a two-channel 3-D feature-extraction network and a feature classifier. This network extracts 3-D morphology features from the tensor blocks using 3-D discrete wavelet transform and a 3-D convolutional neural network (CNN). The extracted features are then fused using robust sparse linear discriminant analysis (RSLDA), and an extreme learning machine processes the fusion features to produce the final results. The experimental results show that the proposed algorithm outperforms state-of-the-art methods in terms of detection rate and false alarm rate.
{"title":"Shipborne HFSWR Target Detection in Sea Clutter Regions Based on 3-D Feature Fusion","authors":"Jiangnan Zhong;Gangsheng Li;Ling Zhang;Lanjun Liu;Q. M. Jonathan Wu","doi":"10.1109/TRS.2024.3472075","DOIUrl":"https://doi.org/10.1109/TRS.2024.3472075","url":null,"abstract":"Shipborne high-frequency surface wave radar (HFSWR) systems face the challenge of sea clutter spreading, which obscures vessel echoes and makes detection difficult. In this article, we propose a novel 3-D target detection algorithm that effectively identifies vessel targets in sea clutter using multidimensional fusion features. The algorithm consists of two stages: 3-D spectrum construction and target detection. In the 3-D spectrum construction stage, the digital narrow beam forming (DNBF) method is combined to transform the range-Doppler (RD) spectrum into a range-Doppler–azimuth 3-D spectrum. In the target detection stage, a two-level cascade target detection algorithm is proposed. At the first level, a 3-D extremum detection algorithm identifies potential vessels in sea clutter from the 3-D spectrum and locates the 3-D tensor blocks containing high-dimensional morphology features of these potential vessels. At the second level, we introduce an intelligent 3-D tensor block classifier, which includes a two-channel 3-D feature-extraction network and a feature classifier. This network extracts 3-D morphology features from the tensor blocks using 3-D discrete wavelet transform and a 3-D convolutional neural network (CNN). The extracted features are then fused using robust sparse linear discriminant analysis (RSLDA), and an extreme learning machine processes the fusion features to produce the final results. The experimental results show that the proposed algorithm outperforms state-of-the-art methods in terms of detection rate and false alarm rate.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1109/TRS.2024.3471857
Jieru Ding;Xinghui Wu;Min Wang;Steven Gao
Automotive radar point-cloud imaging plays an important role in advanced driver assistant systems (ADASs), and most vehicle-mounted radars improve the angular resolution by the time-division multiplexing multiple-input and multiple-output (TDM-MIMO). However, the performance of TDM-MIMO radar suffers seriously from the transmitted energy loss, serious Doppler ambiguity, and the coupling phase induced by the switching delay. In this article, we have proposed a 4-D point-cloud imaging method based on the Doppler division multiplier access (DDMA) MIMO radar and have used the sparse array to balance the contradiction between the Doppler ambiguity and the angle resolution. First, a 2-D hybrid sparse array, both the transmitted array and the received array being sparse linear array (SLA), is designed to mitigate the Doppler ambiguity to a certain extent. Sequentially, targets’ locations in space are been focused by taking advantage of the low rankness of the snapshot matrix, and accordingly, facing the problem of decreased signal-to-noise ratio (SNR) directly by the hybrid sparse snapshot matrix, we have proposed jointly low rankness and sparsity based on the matrix factorization (JLSMF) algorithm to obtain the uniform snapshot matrix and the sparse locations of scattering points. Compared with previous achievements, the proposed algorithm has a better performance, lower computation complexity, smaller recovery error, and so on. Finally, simulation experiments have validated the effectiveness of the proposed algorithm. Besides, the proposed algorithm has great reference value in other fields, such as inverse synthetic aperture radar (ISAR), magnetic resonance imaging, and so on.
{"title":"High-Resolution Point-Cloud Imaging With Doppler Division MIMO Radar Based on the 2-D Hybrid Sparse Array","authors":"Jieru Ding;Xinghui Wu;Min Wang;Steven Gao","doi":"10.1109/TRS.2024.3471857","DOIUrl":"https://doi.org/10.1109/TRS.2024.3471857","url":null,"abstract":"Automotive radar point-cloud imaging plays an important role in advanced driver assistant systems (ADASs), and most vehicle-mounted radars improve the angular resolution by the time-division multiplexing multiple-input and multiple-output (TDM-MIMO). However, the performance of TDM-MIMO radar suffers seriously from the transmitted energy loss, serious Doppler ambiguity, and the coupling phase induced by the switching delay. In this article, we have proposed a 4-D point-cloud imaging method based on the Doppler division multiplier access (DDMA) MIMO radar and have used the sparse array to balance the contradiction between the Doppler ambiguity and the angle resolution. First, a 2-D hybrid sparse array, both the transmitted array and the received array being sparse linear array (SLA), is designed to mitigate the Doppler ambiguity to a certain extent. Sequentially, targets’ locations in space are been focused by taking advantage of the low rankness of the snapshot matrix, and accordingly, facing the problem of decreased signal-to-noise ratio (SNR) directly by the hybrid sparse snapshot matrix, we have proposed jointly low rankness and sparsity based on the matrix factorization (JLSMF) algorithm to obtain the uniform snapshot matrix and the sparse locations of scattering points. Compared with previous achievements, the proposed algorithm has a better performance, lower computation complexity, smaller recovery error, and so on. Finally, simulation experiments have validated the effectiveness of the proposed algorithm. Besides, the proposed algorithm has great reference value in other fields, such as inverse synthetic aperture radar (ISAR), magnetic resonance imaging, and so on.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1048-1061"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1109/TRS.2024.3471696
Hiroki Mori;Ryota Sekiya
Some existing radar imaging apparatuses require a large number of transmitting and receiving antennas and, thus, impose stringent requirements on hardware design. In this article, we propose a millimeter-wave radar imaging method that combines multistatic radar with coprime measurements, to significantly reduce the number of antennas and the amount of data. The proposed radar array system replaces every monostatic radar with a pair comprising a separated transmitter and receiver along with phase corrections. Since multiple receivers can simultaneously receive the reflection when a transmitter emits a signal and then efficiently create virtual subarrays obtained by coprime measurements, the proposed radar array system can further reduce the number of measurements (antennas) and the amount of data compared with the existing schemes. Our proposal is demonstrated through simulations and experiments, and the results indicate that the proposed radar array system is advantageous in implementation in terms of hardware design and data acquisition time.
{"title":"Millimeter-Wave Radar Imaging Using Multistatic Coprime Array Configuration for Invisible Object Testing","authors":"Hiroki Mori;Ryota Sekiya","doi":"10.1109/TRS.2024.3471696","DOIUrl":"https://doi.org/10.1109/TRS.2024.3471696","url":null,"abstract":"Some existing radar imaging apparatuses require a large number of transmitting and receiving antennas and, thus, impose stringent requirements on hardware design. In this article, we propose a millimeter-wave radar imaging method that combines multistatic radar with coprime measurements, to significantly reduce the number of antennas and the amount of data. The proposed radar array system replaces every monostatic radar with a pair comprising a separated transmitter and receiver along with phase corrections. Since multiple receivers can simultaneously receive the reflection when a transmitter emits a signal and then efficiently create virtual subarrays obtained by coprime measurements, the proposed radar array system can further reduce the number of measurements (antennas) and the amount of data compared with the existing schemes. Our proposal is demonstrated through simulations and experiments, and the results indicate that the proposed radar array system is advantageous in implementation in terms of hardware design and data acquisition time.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1036-1047"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524139","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}
Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.
{"title":"Co-Prime Sampling-Based Time-Delay Estimation for Roadway Survey by Ground Penetrating Radar via Off-Grid Sparse Bayesian Learning","authors":"Jingjing Pan;Huimin Pan;Meng Sun;Yide Wang;Vincent Baltazart;Xudong Dong;Jun Zhao;Xiaofei Zhang;Hing Cheung So","doi":"10.1109/TRS.2024.3467993","DOIUrl":"https://doi.org/10.1109/TRS.2024.3467993","url":null,"abstract":"Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply a uniform sampling strategy for TDE, which requires numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from the thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy-based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred to co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with a co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and noncircularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time delays and thickness.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"966-978"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397287","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}
This article presents a novel constant false alarm rate (CFAR) compressed detection approach for human detection using the impulse radio ultrawideband (IR-UWB) radar. The associated Xampling scheme operates way below the Nyquist limit and is designed to minimize the sensing matrix coherence (SMC), without increasing the implementation complexity. The proposed signal-processing architecture aims to detect both moving and stationary people in the framework of heavy-cluttered use cases, such as smart factory indoor environments. To address this challenge, we not only rely on standard radar signal processing, including moving target indicator (MTI) filtering, noise whitening, and Doppler focusing (DF), but also introduce two new algorithms for joint sparse reconstruction (SR) and CFAR detection, in fast-time and range-Doppler domains, respectively. We propose a specific detection statistic, which is proven to be appropriate for both algorithms, its distribution being identified and then validated by standard goodness-of-fit tests. Moreover, it enables reducing the CFAR scheme complexity, since the associated detection threshold is invariant to the noise power, thus making unnecessary its estimation. The proposed approach is finally validated using both simulated and experimentally measured data in an Industry 4.0 indoor environment, for several canonical scenarios. The effectiveness of our CFAR compressed detection algorithms for human detection is thus fully demonstrated, and their performance is assessed and compared to that obtained by signal processing at the Nyquist sampling rate.
{"title":"CFAR Compressed Detection in Heavy-Cluttered Indoor Environments Using IR-UWB Radar: New Experimentally Supported Results","authors":"Zaynab Baydoun;Roua Youssef;Emanuel Radoi;Stéphane Azou;Tina Yaacoub","doi":"10.1109/TRS.2024.3467549","DOIUrl":"https://doi.org/10.1109/TRS.2024.3467549","url":null,"abstract":"This article presents a novel constant false alarm rate (CFAR) compressed detection approach for human detection using the impulse radio ultrawideband (IR-UWB) radar. The associated Xampling scheme operates way below the Nyquist limit and is designed to minimize the sensing matrix coherence (SMC), without increasing the implementation complexity. The proposed signal-processing architecture aims to detect both moving and stationary people in the framework of heavy-cluttered use cases, such as smart factory indoor environments. To address this challenge, we not only rely on standard radar signal processing, including moving target indicator (MTI) filtering, noise whitening, and Doppler focusing (DF), but also introduce two new algorithms for joint sparse reconstruction (SR) and CFAR detection, in fast-time and range-Doppler domains, respectively. We propose a specific detection statistic, which is proven to be appropriate for both algorithms, its distribution being identified and then validated by standard goodness-of-fit tests. Moreover, it enables reducing the CFAR scheme complexity, since the associated detection threshold is invariant to the noise power, thus making unnecessary its estimation. The proposed approach is finally validated using both simulated and experimentally measured data in an Industry 4.0 indoor environment, for several canonical scenarios. The effectiveness of our CFAR compressed detection algorithms for human detection is thus fully demonstrated, and their performance is assessed and compared to that obtained by signal processing at the Nyquist sampling rate.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"991-1006"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TRS.2024.3466134
Yuan-Peng Zhang;Zhi-Hao Wang;Tai-Yang Liu;Yan Xie;Ying Luo
Heterogeneous radar network systems can provide multiband and multiangle information about targets, enhancing the ability to recognize space targets. This article proposes a space target recognition method based on a bidirectional gated recurrent unit (BiGRU)-Transformer and dual graph fusion (BiGT-DGF) network. Through a temporal information extraction subnetwork, the BiGRU and Transformer are used to dynamically model a radar cross section (RCS) time series under multiple bands and angles, effectively exploiting both the local and global temporal dependencies. Through a spatial information extraction subnetwork, which integrates predefined graphs with self-adaptive graphs, the spatial dependencies between various radars are dynamically and adaptively captured. On this basis, the prediction output layer utilizes the spatiotemporal information extracted by the above two subnetworks to effectively recognize space targets. The experimental results show that the proposed method can reliably recognize space targets even under low signal-to-noise ratios (SNRs) and low pulse repetition frequencies.
{"title":"Space Target Recognition Based on Radar Network Systems With BiGRU-Transformer and Dual Graph Fusion Network","authors":"Yuan-Peng Zhang;Zhi-Hao Wang;Tai-Yang Liu;Yan Xie;Ying Luo","doi":"10.1109/TRS.2024.3466134","DOIUrl":"https://doi.org/10.1109/TRS.2024.3466134","url":null,"abstract":"Heterogeneous radar network systems can provide multiband and multiangle information about targets, enhancing the ability to recognize space targets. This article proposes a space target recognition method based on a bidirectional gated recurrent unit (BiGRU)-Transformer and dual graph fusion (BiGT-DGF) network. Through a temporal information extraction subnetwork, the BiGRU and Transformer are used to dynamically model a radar cross section (RCS) time series under multiple bands and angles, effectively exploiting both the local and global temporal dependencies. Through a spatial information extraction subnetwork, which integrates predefined graphs with self-adaptive graphs, the spatial dependencies between various radars are dynamically and adaptively captured. On this basis, the prediction output layer utilizes the spatiotemporal information extracted by the above two subnetworks to effectively recognize space targets. The experimental results show that the proposed method can reliably recognize space targets even under low signal-to-noise ratios (SNRs) and low pulse repetition frequencies.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"950-965"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368557","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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