Synthetic aperture radar (SAR) imaging provides a method for increasing the resolution of small and low-cost frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar sensors. Using SAR images as an alternative to traditional point cloud-based representations of the environment may improve the performance of simultaneous localization and mapping (SLAM) algorithms for mobile robots. This article presents the details of an indoor mobile robot system that fuses inertial measurement unit (IMU) measurements and radar velocity estimates from an incoherent network of automotive radar sensors using an error-state Kalman filter (ESKF). This trajectory estimate is used to create surround-view SAR images of the robot’s operating environment. The obtained trajectory accuracy is compared against a laboratory reference system, and high-resolution SAR imaging results are presented. The measurement results provide insights into the challenges of robotic millimeter-wave imaging in indoor scenarios.
{"title":"RIO-SAR: Synthetic Aperture Radar Imaging of Indoor Scenes Based on Radar-Inertial Odometry Using a Mobile Robot","authors":"Yuma Elia Ritterbusch;Johannes Fink;Christian Waldschmidt","doi":"10.1109/TRS.2024.3488474","DOIUrl":"https://doi.org/10.1109/TRS.2024.3488474","url":null,"abstract":"Synthetic aperture radar (SAR) imaging provides a method for increasing the resolution of small and low-cost frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar sensors. Using SAR images as an alternative to traditional point cloud-based representations of the environment may improve the performance of simultaneous localization and mapping (SLAM) algorithms for mobile robots. This article presents the details of an indoor mobile robot system that fuses inertial measurement unit (IMU) measurements and radar velocity estimates from an incoherent network of automotive radar sensors using an error-state Kalman filter (ESKF). This trajectory estimate is used to create surround-view SAR images of the robot’s operating environment. The obtained trajectory accuracy is compared against a laboratory reference system, and high-resolution SAR imaging results are presented. The measurement results provide insights into the challenges of robotic millimeter-wave imaging in indoor scenarios.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1200-1213"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777885","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}
Autonomous driving vehicles are being more and more popular in the community with the rise of artificial intelligence systems. However, in the context of airborne navigation, it remains a challenge, especially during landing maneuver. In order to operate in all conditions (weather, day, and night) and in all airports, we propose a runway localization method based on images acquired by an onboard radar. The proposed algorithm is a radar data segmentation method designed for use by an aircraft, as an on-board system, to provide the pilot, whether human or automatic, with a runway location prediction to facilitate and secure the landing maneuver. This article describes the acquisition and labeling of a large-scale real dataset over 18 airports in France and Switzerland, and the proposition of an attention-based deep recurrent neural network (RNN) for semantic segmentation of 4-D radar data acquired during a landing maneuver. This end-to-end trainable neural network combines attention mechanisms adapted to the geometry of an approach scene, with the exploitation of spatial-temporal information via recursive cells, all being associated with a convolutional segmentation model (patent pending). This article proposes a sensitivity analysis of Lyon’s airport to tune the hyperparameters, demonstrating the interest in adapting the attention sequence, especially through the shape of patches. The experimental results have shown the benefit of each block in the model. Extensive experiments on the other available airports have allowed validating the potential of the proposed network. Experiments have shown a considerable gain of about 0.17 on the DICE score associated with the exploitation of attention mechanisms and recursive cells and a gain of 0.1 compared to the SegFormer-B0 model.
{"title":"Attention-Based Deep Recurrent Neural Network for Semantic Segmentation of 4-D Radar Data Acquired During Landing Maneuver","authors":"Solène Vilfroy;Thierry Urruty;Philippe Carré;Jean-Philippe Lebrat;Lionel Bombrun","doi":"10.1109/TRS.2024.3488475","DOIUrl":"https://doi.org/10.1109/TRS.2024.3488475","url":null,"abstract":"Autonomous driving vehicles are being more and more popular in the community with the rise of artificial intelligence systems. However, in the context of airborne navigation, it remains a challenge, especially during landing maneuver. In order to operate in all conditions (weather, day, and night) and in all airports, we propose a runway localization method based on images acquired by an onboard radar. The proposed algorithm is a radar data segmentation method designed for use by an aircraft, as an on-board system, to provide the pilot, whether human or automatic, with a runway location prediction to facilitate and secure the landing maneuver. This article describes the acquisition and labeling of a large-scale real dataset over 18 airports in France and Switzerland, and the proposition of an attention-based deep recurrent neural network (RNN) for semantic segmentation of 4-D radar data acquired during a landing maneuver. This end-to-end trainable neural network combines attention mechanisms adapted to the geometry of an approach scene, with the exploitation of spatial-temporal information via recursive cells, all being associated with a convolutional segmentation model (patent pending). This article proposes a sensitivity analysis of Lyon’s airport to tune the hyperparameters, demonstrating the interest in adapting the attention sequence, especially through the shape of patches. The experimental results have shown the benefit of each block in the model. Extensive experiments on the other available airports have allowed validating the potential of the proposed network. Experiments have shown a considerable gain of about 0.17 on the DICE score associated with the exploitation of attention mechanisms and recursive cells and a gain of 0.1 compared to the SegFormer-B0 model.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1135-1147"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636292","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}
Radio frequency (RF) sensing applications such as RF waveform classification and human activity recognition (HAR) demand real-time processing capabilities. Current state-of-the-art techniques often require a two-stage process for classification: first, computing a time-frequency (TF) transform, and then applying machine learning (ML) using the TF domain as the input for classification. This process hinders the opportunities for real-time classification. Consequently, there is a growing interest in direct classification from raw IQ-RF data streams. Applying existing deep learning (DL) techniques directly to the raw IQ radar data has shown limited accuracy for various applications. To address this, this article proposes to learn the parameters of structured functions as filterbanks within complex-valued (CV) neural network architectures. The initial layer of the proposed architecture features CV parameterized learnable filters (PLFs) that directly work on the raw data and generate frequency-related features based on the structured function of the filter. This work presents four different PLFs: Sinc, Gaussian, Gammatone, and Ricker functions, which demonstrate different types of frequency-domain bandpass filtering to show their effectiveness in RF data classification directly from raw IQ radar data. Learning structured filters also enhances interpretability and understanding of the network. The proposed approach was tested on both experimental and synthetic datasets for sign and modulation recognition. The PLF-based models achieved an average of 47% improvement in classification accuracy compared with a 1-D convolutional neural network (CNN) on raw RF data and an average 7% improvement over CNNs with real-valued learnable filters for the experimental dataset. It also matched the accuracy of a 2-D CNN applied to micro-Doppler ( $mu $ D) spectrograms while reducing computational latency by around 75%. These results demonstrate the potential of the proposed model for a range of RF sensing applications with enhanced accuracy and computational efficiency.
{"title":"PLFNets: Interpretable Complex-Valued Parameterized Learnable Filters for Computationally Efficient RF Classification","authors":"Sabyasachi Biswas;Cemre Omer Ayna;Ali Cafer Gurbuz","doi":"10.1109/TRS.2024.3486183","DOIUrl":"https://doi.org/10.1109/TRS.2024.3486183","url":null,"abstract":"Radio frequency (RF) sensing applications such as RF waveform classification and human activity recognition (HAR) demand real-time processing capabilities. Current state-of-the-art techniques often require a two-stage process for classification: first, computing a time-frequency (TF) transform, and then applying machine learning (ML) using the TF domain as the input for classification. This process hinders the opportunities for real-time classification. Consequently, there is a growing interest in direct classification from raw IQ-RF data streams. Applying existing deep learning (DL) techniques directly to the raw IQ radar data has shown limited accuracy for various applications. To address this, this article proposes to learn the parameters of structured functions as filterbanks within complex-valued (CV) neural network architectures. The initial layer of the proposed architecture features CV parameterized learnable filters (PLFs) that directly work on the raw data and generate frequency-related features based on the structured function of the filter. This work presents four different PLFs: Sinc, Gaussian, Gammatone, and Ricker functions, which demonstrate different types of frequency-domain bandpass filtering to show their effectiveness in RF data classification directly from raw IQ radar data. Learning structured filters also enhances interpretability and understanding of the network. The proposed approach was tested on both experimental and synthetic datasets for sign and modulation recognition. The PLF-based models achieved an average of 47% improvement in classification accuracy compared with a 1-D convolutional neural network (CNN) on raw RF data and an average 7% improvement over CNNs with real-valued learnable filters for the experimental dataset. It also matched the accuracy of a 2-D CNN applied to micro-Doppler (\u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000D) spectrograms while reducing computational latency by around 75%. These results demonstrate the potential of the proposed model for a range of RF sensing applications with enhanced accuracy and computational efficiency.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1102-1111"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595067","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 : 2024-10-23DOI: 10.1109/TRS.2024.3485578
Ignacio Roldan;Andras Palffy;Julian F. P. Kooij;Dariu M. Gavrila;Francesco Fioranelli;Alexander Yarovoy
The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchronized lidar data are used as ground truth to train a neural network (NN) with only radar data as input. To this end, the novel, large-scale, real-life, and multisensor RaDelft dataset has been recorded using a demonstrator vehicle in different locations in the city of Delft, The Netherlands. The dataset, as well as the documentation and example code, is publicly available for those researchers in the field of automotive radar or machine perception. The proposed data-driven detector can generate lidar-like point clouds (PCs) using only radar data from a high-resolution system, which preserves the shape and size of extended targets. The results are compared against conventional constant false alarm rate (CFAR) detectors as well as variations of the method to emulate the available approaches in the literature, using the probability of detection, the probability of false alarm, and the Chamfer distance (CD) as performance metrics. Moreover, an ablation study was carried out to assess the impact of Doppler and temporal information on detection performance. The proposed method outperforms different baselines in terms of CD, achieving a reduction of 77% against conventional CFAR detectors and 28% against the modified state-of-the-art deep learning (DL)-based approaches.
{"title":"A Deep Automotive Radar Detector Using the RaDelft Dataset","authors":"Ignacio Roldan;Andras Palffy;Julian F. P. Kooij;Dariu M. Gavrila;Francesco Fioranelli;Alexander Yarovoy","doi":"10.1109/TRS.2024.3485578","DOIUrl":"https://doi.org/10.1109/TRS.2024.3485578","url":null,"abstract":"The detection of multiple extended targets in complex environments using high-resolution automotive radar is considered. A data-driven approach is proposed where unlabeled synchronized lidar data are used as ground truth to train a neural network (NN) with only radar data as input. To this end, the novel, large-scale, real-life, and multisensor RaDelft dataset has been recorded using a demonstrator vehicle in different locations in the city of Delft, The Netherlands. The dataset, as well as the documentation and example code, is publicly available for those researchers in the field of automotive radar or machine perception. The proposed data-driven detector can generate lidar-like point clouds (PCs) using only radar data from a high-resolution system, which preserves the shape and size of extended targets. The results are compared against conventional constant false alarm rate (CFAR) detectors as well as variations of the method to emulate the available approaches in the literature, using the probability of detection, the probability of false alarm, and the Chamfer distance (CD) as performance metrics. Moreover, an ablation study was carried out to assess the impact of Doppler and temporal information on detection performance. The proposed method outperforms different baselines in terms of CD, achieving a reduction of 77% against conventional CFAR detectors and 28% against the modified state-of-the-art deep learning (DL)-based approaches.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1062-1075"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595131","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 : 2024-10-23DOI: 10.1109/TRS.2024.3485067
Bijan G. Mobasseri
It is well-known that the phase of the beat signal in frequency modulated continuous wave (FMCW) radar contains information about the range. However, $2pi $ phase wrapping limits the maximum unambiguous range to an unrealistically short distance. As a result, phase has not been widely used as a means for range finding. In this work, we propose a dual-frequency chirp waveform formed by modulating a baseband chirp onto two subcarriers, combing them then following by main carrier modulation. This approach means that each subcarrier creates its own beat signal represented by rotating phasors. Each phase angle carries information about the delay but is subject to phase wrap very quickly. The obvious solution is to limit delay by choosing a working range of unrealistically short distances. However, it can be shown that the phase differences between the two phasors could be worked out in such a way as to cancel phase wrap. A waveform design parameter in the form of the spread-delay product is identified that when properly chosen will mitigate phase wrap before it occurs. The spread-delay term is the product of subcarrier frequency spacing and the expected delay. There are no restrictions on choosing the spacing; hence, the waveform can be tuned to match all expected delays. Simulations are run to show that the concept works for both short ranges, as in automotive radar, and long-range surveillance such as air traffic control.
{"title":"A Solution to the Wrapped Phase Problem by Dual Subcarrier-Modulated Chirps","authors":"Bijan G. Mobasseri","doi":"10.1109/TRS.2024.3485067","DOIUrl":"https://doi.org/10.1109/TRS.2024.3485067","url":null,"abstract":"It is well-known that the phase of the beat signal in frequency modulated continuous wave (FMCW) radar contains information about the range. However, \u0000<inline-formula> <tex-math>$2pi $ </tex-math></inline-formula>\u0000 phase wrapping limits the maximum unambiguous range to an unrealistically short distance. As a result, phase has not been widely used as a means for range finding. In this work, we propose a dual-frequency chirp waveform formed by modulating a baseband chirp onto two subcarriers, combing them then following by main carrier modulation. This approach means that each subcarrier creates its own beat signal represented by rotating phasors. Each phase angle carries information about the delay but is subject to phase wrap very quickly. The obvious solution is to limit delay by choosing a working range of unrealistically short distances. However, it can be shown that the phase differences between the two phasors could be worked out in such a way as to cancel phase wrap. A waveform design parameter in the form of the spread-delay product is identified that when properly chosen will mitigate phase wrap before it occurs. The spread-delay term is the product of subcarrier frequency spacing and the expected delay. There are no restrictions on choosing the spacing; hence, the waveform can be tuned to match all expected delays. Simulations are run to show that the concept works for both short ranges, as in automotive radar, and long-range surveillance such as air traffic control.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1089-1101"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595112","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 : 2024-10-23DOI: 10.1109/TRS.2024.3484632
Jun Sun;Ye Yuan;Maria Sabrina Greco;Fulvio Gini
The rapid development of cooperative techniques and anti-jamming methods in modern radar systems has significantly improved the mission performance and survivability of radars. In practical applications, the single jammer system cannot cope with the cooperative technology of the radar system due to its single interference pattern and spatial angle. To combat distributed radar systems, in this article, we construct and solve a resource management problem with the goal of minimizing the false target rejection probability, while being constrained by the deception jamming power budget of the multijammer system. First, the posterior Cramér-Rao lower bounds (PCRLBs) including target state and deception parameters related to the radar system under deception jamming are derived. On this basis, a false target discriminator is designed and the corresponding rejection probability is derived, which is regarded as the metric to assess the deception jamming performance. Then, the deception jamming power scheduling (DJPS) problem of the multijammer system for cooperatively combating distributed radar systems is constructed, subject to the system resource configurations. Due to the nonconvexity of the false target rejection probability, the formulated problem is inherently nonconvex. To effectively address this problem, a modified particle swarm optimization (MPSO) algorithm is presented. Numerical simulations verify that the proposed strategy and MPSO algorithm show superior deception jamming performance in combating distributed radar systems.
{"title":"Coordinated Deception Jamming Power Scheduling for Multijammer Systems Against Distributed Radar Systems","authors":"Jun Sun;Ye Yuan;Maria Sabrina Greco;Fulvio Gini","doi":"10.1109/TRS.2024.3484632","DOIUrl":"https://doi.org/10.1109/TRS.2024.3484632","url":null,"abstract":"The rapid development of cooperative techniques and anti-jamming methods in modern radar systems has significantly improved the mission performance and survivability of radars. In practical applications, the single jammer system cannot cope with the cooperative technology of the radar system due to its single interference pattern and spatial angle. To combat distributed radar systems, in this article, we construct and solve a resource management problem with the goal of minimizing the false target rejection probability, while being constrained by the deception jamming power budget of the multijammer system. First, the posterior Cramér-Rao lower bounds (PCRLBs) including target state and deception parameters related to the radar system under deception jamming are derived. On this basis, a false target discriminator is designed and the corresponding rejection probability is derived, which is regarded as the metric to assess the deception jamming performance. Then, the deception jamming power scheduling (DJPS) problem of the multijammer system for cooperatively combating distributed radar systems is constructed, subject to the system resource configurations. Due to the nonconvexity of the false target rejection probability, the formulated problem is inherently nonconvex. To effectively address this problem, a modified particle swarm optimization (MPSO) algorithm is presented. Numerical simulations verify that the proposed strategy and MPSO algorithm show superior deception jamming performance in combating distributed radar systems.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1076-1088"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595130","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 : 2024-10-21DOI: 10.1109/TRS.2024.3483772
Tianfu Zhang;Yunkai Deng;Yongliang Wang
Due to the platform characteristics of space-based early warning radar (SBEWR), the system exhibits a high degree of freedom (DOF) in receiving sea and ground clutter, which complicates the achievement of adequate adaptive clutter suppression performance. To address this challenge, this article proposes a joint dimensionality-reduced adaptive clutter suppression method based on a frequency diverse array (FDA) for SBEWR. First, a pulse parameter joint design scheme tailored to FDA-SBEWR is introduced, which mitigates the impact of range ambiguity on received clutter. Second, a joint dimensionality-reduced structure design method is developed, focusing on received clutter data. This approach significantly reduces the computational resource demands of the adaptive system while satisfying the DOF requirements for signal processing, thereby ensuring excellent clutter suppression performance for SBEWR. The simulation results demonstrate the effectiveness of the proposed method.
{"title":"A Novel Joint Dimensionality-Reduced Adaptive Clutter Suppression Method for Space-Based Early Warning Radar Utilizing Frequency Diversity Array","authors":"Tianfu Zhang;Yunkai Deng;Yongliang Wang","doi":"10.1109/TRS.2024.3483772","DOIUrl":"https://doi.org/10.1109/TRS.2024.3483772","url":null,"abstract":"Due to the platform characteristics of space-based early warning radar (SBEWR), the system exhibits a high degree of freedom (DOF) in receiving sea and ground clutter, which complicates the achievement of adequate adaptive clutter suppression performance. To address this challenge, this article proposes a joint dimensionality-reduced adaptive clutter suppression method based on a frequency diverse array (FDA) for SBEWR. First, a pulse parameter joint design scheme tailored to FDA-SBEWR is introduced, which mitigates the impact of range ambiguity on received clutter. Second, a joint dimensionality-reduced structure design method is developed, focusing on received clutter data. This approach significantly reduces the computational resource demands of the adaptive system while satisfying the DOF requirements for signal processing, thereby ensuring excellent clutter suppression performance for SBEWR. The simulation results demonstrate the effectiveness of the proposed method.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1123-1134"},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Radar 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.
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