IEEE Transactions on Radar Systems最新文献

{"title":"Radar-Based Tremor Quantification Using Deep Learning for Improved Parkinson’s and Palliative Care Assessment","authors":"Desar Mejdani;Johanna Bräunig;Stefan G. GrießHammer;Daniel Krauss;Tobias Steigleder;Lukas Engel;Jelena Jukic;Anna Rozhdestvenskaya;Jürgen Winkler;Bjoern Eskofier;Christoph Ostgathe;Martin Vossiek","doi":"10.1109/TRS.2024.3494473","DOIUrl":"https://doi.org/10.1109/TRS.2024.3494473","url":null,"abstract":"Tremor is one of the most prevalent movement disorders, which is especially observed in patients with Parkinson’s disease (PD) and other conditions common in palliative care (PC). Effective treatment and monitoring of disease progression are crucial in the context of PC for patients suffering from movement disorders. To this aim, accurate and continuous detection and assessment of tremor characteristics, such as the tremor frequency, is required. Current evaluations by clinicians conducted during sporadic consultations are subjective and intermittent. Radar sensors provide continuous, objective evaluations of tremor motion in patient monitoring, offering a contactless, light-independent, and privacy-preserving method that directly measures tremor’s radial motion through the Doppler effect. As previous radar-based research lacks continuous tremor monitoring in realistic scenarios, this study uses a frequency-modulated continuous-wave (FMCW) radar to detect subtle tremor motions and estimates their frequencies amid challenges such as large body motion interference in a clinical setting. Seventeen healthy participants were instructed to mimic tremors in their right hand while performing three diagnostics movements frequently used in tremor assessment, and two activities that were inspired by common daily tasks encountered in PC settings. Tremor detection and frequency estimation was enabled using suitable radar signal preprocessing followed by a neural network comprising convolutional and recurrent layers. Reference frequencies were obtained from an inertial measurement unit (IMU) attached to the participants’ right hands. Cross-validation revealed a mean absolute error (MAE) of 1.47 Hz in radar-based frequency estimation compared with the reference and a 90% accuracy in distinguishing the presence or absence of tremor, highlighting the proposed approach’s high potential for future tremor assessment.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1174-1185"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672059","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}

{"title":"Performance Degradation of DOA Estimation in Distributed Radar Networks Under Near-Field Influence","authors":"Yi Li;Weijie Xia;Lingzhi Zhu;Jianjiang Zhou;Yongyan Chu;Wogong Zhang;Jie Zhang","doi":"10.1109/TRS.2024.3493037","DOIUrl":"https://doi.org/10.1109/TRS.2024.3493037","url":null,"abstract":"In striving for optimal performance in distributed radar networks tailored for short-range applications, conventional direction-of-arrival (DOA) estimation often proves inadequate. The presence of close-in targets introduces a mismatch in the radar echo model, challenging the validity of far-field (FF) assumptions. To address this problem, we have developed a misspecified Cramér-Rao bound (MCRB) for DOA estimation in distributed radar networks influenced by near-field (NF) effects. The derivation aids in understanding potential performance degradations associated with the mean-squared error (mse) of a misspecified maximum-likelihood estimator. Through comprehensive analysis, we explore the interaction between the usual Cramér-Rao bound (CRB) and the MCRB. Moreover, we conduct a meticulous investigation into the relationship between these bounds, target parameters, and system architecture. Our examination significantly advances radar performance in practical scenarios, providing valuable insights to inform the design and configuration of distributed radar systems.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1148-1159"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671121","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}

{"title":"Outlier Detection Enhancement in Heterogeneous Environments Through a Novel Training Set Selection Framework","authors":"Yongchan Gao;Kexuan Cui;Danilo Orlando;Chen Zhang;Guisheng Liao;Lei Zuo","doi":"10.1109/TRS.2024.3491795","DOIUrl":"https://doi.org/10.1109/TRS.2024.3491795","url":null,"abstract":"Most training set selection (TSS) methods are based on data processing methods. These methods have improved the state-of-the-art in clutter suppression under heterogeneous condition; however, TSS for heterogeneous and complex environments has rarely been investigated, especially for large outliers. This problem arises in situations such as isolated elevation points, spike effects of mountains, and urban-rural interfaces in actual radar operating environments. To address such a problem, this article proposes a novel enhanced outlier detection framework that deals with TSS in the presence of an unknown number of multiple outliers. First, the design of the overall structure of the TSS framework is proposed. We decompose the actual radar returns into four components and further integrate them into the TSS framework. The proposed framework uses the statistical characteristics of the returns from the range cells as a classification criterion. A deep neural network is devised to extract these statistical characteristics for outlier detection. The loss function and learning rate selection of the proposed TSS framework are, furthermore, specified. Then, the classification model for the four signal components is presented. To validate this framework, we use a real radar dataset sampled from heterogeneous environments and characterize signals in real radar scenarios. Experimental results demonstrate that the proposed framework significantly improves the accuracy of outlier detection in comparison with the traditional heterogeneous TSS method. In addition, our framework can further distinguish the interference outliers from the target echoes.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1160-1173"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142671122","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}