Pub Date : 2024-12-12DOI: 10.1109/TRS.2024.3516413
Wending Li;Zhihuo Xu;Liu Chu;Quan Shi;Robin Braun;Jiajia Shi
The state of drowsiness significantly affects work efficiency and productivity, increasing the risk of accidents and mishaps. Radar-based detection technology offers significant advantages in drowsiness detection, providing a noninvasive and reliable method based on vital sign tracking and physiological feature extraction. However, the classification of sleepiness levels is often simple and the detection accuracy is limited. This study proposes a frequency-modulated continuous-wave (FMCW) radar-based system with a convolutional adaptive pooling attention gated-recurrent-unit (CAPA-GRU) network to enhance detection accuracy and precisely determine levels of radar-based drowsiness detection. First, an FMCW radar is used to obtain breathing and heartbeat signals, and the radar signals are processed through the wavelet transform method to obtain highly accurate physiological characteristics. Then, the vital sign signals are analyzed both in the time and frequency domains, and the optimal input data is obtained by combining the characteristic data. Also, the CAPA-GRU, comprising a convolutional neural network (CNN), a gated-recurrent-unit (GRU), and a convolutional adaptive average pooling (CAA) module, is proposed for drowsiness classification and monitoring. The experimental results show that the proposed method achieves multistage sleepiness detection based on FMCW radar and achieves excellent results in low classification. The proposed network has excellent performance and certain robustness. Experiments conducted with cross-validation on a self-collected dataset show that the proposed method achieved 90.11% accuracy in binary classification, 80.50% accuracy in ternary classification, and 58.17% accuracy in quinary classification and the study also used a public data set for sleepiness detection, and the detection accuracy reached 97.34%.
{"title":"FMCW Radar-Based Drowsiness Detection With a Convolutional Adaptive Pooling Attention Gated-Recurrent-Unit Network","authors":"Wending Li;Zhihuo Xu;Liu Chu;Quan Shi;Robin Braun;Jiajia Shi","doi":"10.1109/TRS.2024.3516413","DOIUrl":"https://doi.org/10.1109/TRS.2024.3516413","url":null,"abstract":"The state of drowsiness significantly affects work efficiency and productivity, increasing the risk of accidents and mishaps. Radar-based detection technology offers significant advantages in drowsiness detection, providing a noninvasive and reliable method based on vital sign tracking and physiological feature extraction. However, the classification of sleepiness levels is often simple and the detection accuracy is limited. This study proposes a frequency-modulated continuous-wave (FMCW) radar-based system with a convolutional adaptive pooling attention gated-recurrent-unit (CAPA-GRU) network to enhance detection accuracy and precisely determine levels of radar-based drowsiness detection. First, an FMCW radar is used to obtain breathing and heartbeat signals, and the radar signals are processed through the wavelet transform method to obtain highly accurate physiological characteristics. Then, the vital sign signals are analyzed both in the time and frequency domains, and the optimal input data is obtained by combining the characteristic data. Also, the CAPA-GRU, comprising a convolutional neural network (CNN), a gated-recurrent-unit (GRU), and a convolutional adaptive average pooling (CAA) module, is proposed for drowsiness classification and monitoring. The experimental results show that the proposed method achieves multistage sleepiness detection based on FMCW radar and achieves excellent results in low classification. The proposed network has excellent performance and certain robustness. Experiments conducted with cross-validation on a self-collected dataset show that the proposed method achieved 90.11% accuracy in binary classification, 80.50% accuracy in ternary classification, and 58.17% accuracy in quinary classification and the study also used a public data set for sleepiness detection, and the detection accuracy reached 97.34%.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"71-87"},"PeriodicalIF":0.0,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905824","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 : 2024-12-11DOI: 10.1109/TRS.2024.3514840
Sabri Mustafa Kahya;Muhammet Sami Yavuz;Eckehard Steinbach
Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutters. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60-GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro- and micro-range-Doppler images (RDIs). HOOD aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. Since HOOD is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans’ absence and predicts the current scene’s output as “no presence.” HOOD performs well in diverse scenarios, demonstrating its effectiveness across different human activities and situations. On our dataset collected with a 60-GHz short-range FMCW radar with only one transmit (Tx) and three receive antennas, we achieved an average area under the receiver operating characteristic curve (AUROC) of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Importantly, HOOD also perfectly fits on Raspberry Pi 3B+ with a advanced RISC machines (ARM) Cortex-A53 CPU, which showcases its versatility across different hardware environments. Videos of our human presence detection experiments are available at: https://muskahya.github.io/HOOD .
{"title":"HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar","authors":"Sabri Mustafa Kahya;Muhammet Sami Yavuz;Eckehard Steinbach","doi":"10.1109/TRS.2024.3514840","DOIUrl":"https://doi.org/10.1109/TRS.2024.3514840","url":null,"abstract":"Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutters. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60-GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro- and micro-range-Doppler images (RDIs). HOOD aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. Since HOOD is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans’ absence and predicts the current scene’s output as “no presence.” HOOD performs well in diverse scenarios, demonstrating its effectiveness across different human activities and situations. On our dataset collected with a 60-GHz short-range FMCW radar with only one transmit (Tx) and three receive antennas, we achieved an average area under the receiver operating characteristic curve (AUROC) of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Importantly, HOOD also perfectly fits on Raspberry Pi 3B+ with a advanced RISC machines (ARM) Cortex-A53 CPU, which showcases its versatility across different hardware environments. Videos of our human presence detection experiments are available at: \u0000<uri>https://muskahya.github.io/HOOD</uri>\u0000.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"44-56"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890379","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 : 2024-12-11DOI: 10.1109/TRS.2024.3500857
{"title":"IEEE Transactions on Radar Systems Publication Information","authors":"","doi":"10.1109/TRS.2024.3500857","DOIUrl":"https://doi.org/10.1109/TRS.2024.3500857","url":null,"abstract":"","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10783739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810366","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 : 2024-12-09DOI: 10.1109/TRS.2024.3513293
Chandra S. Pappu;Sonny Grooms;Dmitriy Garmatyuk;Thomas L. Carroll;Aubrey N. Beal;Saba Mudaliar
The increased usage of wireless services in the congested electromagnetic spectrum has caused communication systems to contend with the existing operational radar frequency bands. Integrated sensing and communication (ISAC) systems that share the same frequency band and signaling strategies, such as a single radio frequency emission, address these congestion issues. In this work, we propose novel chaotic signal processing techniques and waveform design methods for ISAC systems. First, we consider a family of chaotic oscillators and use their output to encode the information. Next, we multiplex the information carrying chaotic signals to improve the data rates significantly and further use it for ISAC transmission. We show that a simple correlator can accurately decode the information with low bit-error rates. The performance of the multiplexed waveform is robust in the Rician multipath channel. Using correlation and ambiguity function analysis, we claim that the proposed waveforms are excellent candidates for high-resolution radar imaging. We generate synthetic aperture radar (SAR) images using the backprojection algorithm (BPA). The SAR images generated using multiplexed chaos-based waveforms are of similar quality compared to traditionally used linear frequency-modulated waveforms. The most important feature of the proposed multiplexed chaos-based waveforms is their inherent resilience to intentional and nonintentional interference.
{"title":"Interference Resilient Integrated Sensing and Communication Using Multiplexed Chaos","authors":"Chandra S. Pappu;Sonny Grooms;Dmitriy Garmatyuk;Thomas L. Carroll;Aubrey N. Beal;Saba Mudaliar","doi":"10.1109/TRS.2024.3513293","DOIUrl":"https://doi.org/10.1109/TRS.2024.3513293","url":null,"abstract":"The increased usage of wireless services in the congested electromagnetic spectrum has caused communication systems to contend with the existing operational radar frequency bands. Integrated sensing and communication (ISAC) systems that share the same frequency band and signaling strategies, such as a single radio frequency emission, address these congestion issues. In this work, we propose novel chaotic signal processing techniques and waveform design methods for ISAC systems. First, we consider a family of chaotic oscillators and use their output to encode the information. Next, we multiplex the information carrying chaotic signals to improve the data rates significantly and further use it for ISAC transmission. We show that a simple correlator can accurately decode the information with low bit-error rates. The performance of the multiplexed waveform is robust in the Rician multipath channel. Using correlation and ambiguity function analysis, we claim that the proposed waveforms are excellent candidates for high-resolution radar imaging. We generate synthetic aperture radar (SAR) images using the backprojection algorithm (BPA). The SAR images generated using multiplexed chaos-based waveforms are of similar quality compared to traditionally used linear frequency-modulated waveforms. The most important feature of the proposed multiplexed chaos-based waveforms is their inherent resilience to intentional and nonintentional interference.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"26-43"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10781422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880292","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 : 2024-12-02DOI: 10.1109/TRS.2024.3509775
Nicolas C. Kruse;Ronny G. Guendel;Francesco Fioranelli;Alexander Yarovoy
The problem of human activity classification using a distributed network of radar sensors has been considered. A novel sensor fusion method has been proposed that processes data from a network of radar sensors and yields 3-D representations of both reflection intensity and velocity distribution. The formulated method has been verified in an experimental case study, where activity classification was performed using data collected with 14 participants moving in diverse, unconstrained trajectories and executing nine activities. The classification performance of the proposed method has been compared to alternative fusion methods on the same dataset, and a test accuracy and macro $F1$ -score of, respectively, 87.4% and 81.9% have been demonstrated. A feasibility study has also been performed to demonstrate the ability of the proposed method to generate 3-D distributions of intensity and target velocity.
{"title":"Reconstruction of Extended Target Intensity Maps and Velocity Distribution for Human Activity Classification","authors":"Nicolas C. Kruse;Ronny G. Guendel;Francesco Fioranelli;Alexander Yarovoy","doi":"10.1109/TRS.2024.3509775","DOIUrl":"https://doi.org/10.1109/TRS.2024.3509775","url":null,"abstract":"The problem of human activity classification using a distributed network of radar sensors has been considered. A novel sensor fusion method has been proposed that processes data from a network of radar sensors and yields 3-D representations of both reflection intensity and velocity distribution. The formulated method has been verified in an experimental case study, where activity classification was performed using data collected with 14 participants moving in diverse, unconstrained trajectories and executing nine activities. The classification performance of the proposed method has been compared to alternative fusion methods on the same dataset, and a test accuracy and macro \u0000<inline-formula> <tex-math>$F1$ </tex-math></inline-formula>\u0000-score of, respectively, 87.4% and 81.9% have been demonstrated. A feasibility study has also been performed to demonstrate the ability of the proposed method to generate 3-D distributions of intensity and target velocity.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"3 ","pages":"14-25"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798013","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}
In synthetic aperture radar (SAR) information acquisition, target detection is often performed on the basis of the acquired radar images. Under low signal-to-clutter ratio (SCR) or low signal-to-noise ratio (SNR) conditions, detection by images is likely to cause loss of targets. To address this problem, we propose a joint imaging and target detection network based on Bayesian maximum a posteriori (MAP) estimation. The imaging and detection results are, respectively, defined as scene magnitude and detection label, and their joint probability distribution is used in place of the distribution of scene magnitudes. In the MAP estimation, the continuity feature of the detection label is merged into the optimization process, and the imaging and detection results are optimized alternately to get an iterative solution. The iterative solution is then unrolled into a network, which consists of three modules. We first utilize the unrolled fast iterative shrinkage thresholding algorithm (FISTA) method for the image formation module and then incorporate the detection label estimation module and distribution parameter updating module to learn the detection label and the function of distribution parameters. This approach applies prior information for both imaging and detection processes and enables automatic learning of parameters that are difficult to fit. Simulation experiments demonstrate that the method can simultaneously achieve imaging and target detection under strong clutter and strong noise conditions, showing superior performance in both aspects.
{"title":"A Learning Bayesian MAP Framework for Joint SAR Imaging and Target Detection","authors":"Hongyang An;Jianyu Yang;Yuping Xiao;Min Li;Haowen Zuo;Zhongyu Li;Wei Pu;Junjie Wu","doi":"10.1109/TRS.2024.3497057","DOIUrl":"https://doi.org/10.1109/TRS.2024.3497057","url":null,"abstract":"In synthetic aperture radar (SAR) information acquisition, target detection is often performed on the basis of the acquired radar images. Under low signal-to-clutter ratio (SCR) or low signal-to-noise ratio (SNR) conditions, detection by images is likely to cause loss of targets. To address this problem, we propose a joint imaging and target detection network based on Bayesian maximum a posteriori (MAP) estimation. The imaging and detection results are, respectively, defined as scene magnitude and detection label, and their joint probability distribution is used in place of the distribution of scene magnitudes. In the MAP estimation, the continuity feature of the detection label is merged into the optimization process, and the imaging and detection results are optimized alternately to get an iterative solution. The iterative solution is then unrolled into a network, which consists of three modules. We first utilize the unrolled fast iterative shrinkage thresholding algorithm (FISTA) method for the image formation module and then incorporate the detection label estimation module and distribution parameter updating module to learn the detection label and the function of distribution parameters. This approach applies prior information for both imaging and detection processes and enables automatic learning of parameters that are difficult to fit. Simulation experiments demonstrate that the method can simultaneously achieve imaging and target detection under strong clutter and strong noise conditions, showing superior performance in both aspects.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1214-1228"},"PeriodicalIF":0.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797930","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 : 2024-11-08DOI: 10.1109/TRS.2024.3493992
Sen Yuan;Francesco Fioranelli;Alexander G. Yarovoy
The problem of 3-D high-resolution imaging in automotive multiple-input multiple-output (MIMO) side-looking radar using a 1-D array is considered. The concept of motion-enhanced snapshots is introduced to generate larger apertures in the azimuth dimension. For the first time, 3-D imaging capabilities can be achieved with high angular resolution using a 1-D MIMO antenna array, which can alleviate the requirement for large radar systems in autonomous vehicles. The robustness to variations in the vehicle’s movement trajectory is also considered and addressed with relevant compensations in the steering vector. The available degrees of freedom, as well as the signal-to-noise ratio (SNR), are shown to increase with the proposed method compared to conventional imaging approaches. The performance of the algorithm has been studied in simulations, and validated with experimental data collected in a realistic driving scenario.
{"title":"3-D High-Resolution Imaging Algorithm Using 1-D MIMO Array for Autonomous Driving Application","authors":"Sen Yuan;Francesco Fioranelli;Alexander G. Yarovoy","doi":"10.1109/TRS.2024.3493992","DOIUrl":"https://doi.org/10.1109/TRS.2024.3493992","url":null,"abstract":"The problem of 3-D high-resolution imaging in automotive multiple-input multiple-output (MIMO) side-looking radar using a 1-D array is considered. The concept of motion-enhanced snapshots is introduced to generate larger apertures in the azimuth dimension. For the first time, 3-D imaging capabilities can be achieved with high angular resolution using a 1-D MIMO antenna array, which can alleviate the requirement for large radar systems in autonomous vehicles. The robustness to variations in the vehicle’s movement trajectory is also considered and addressed with relevant compensations in the steering vector. The available degrees of freedom, as well as the signal-to-noise ratio (SNR), are shown to increase with the proposed method compared to conventional imaging approaches. The performance of the algorithm has been studied in simulations, and validated with experimental data collected in a realistic driving scenario.","PeriodicalId":100645,"journal":{"name":"IEEE Transactions on Radar Systems","volume":"2 ","pages":"1186-1199"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679409","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}
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.
震颤是最常见的运动障碍之一，尤其见于帕金森病（PD）患者和姑息治疗（PC）中常见的其他疾病。有效治疗和监测疾病进展对于运动障碍患者的姑息治疗至关重要。为此，需要对震颤特征（如震颤频率）进行准确、持续的检测和评估。目前临床医生在零星会诊时进行的评估是主观和间歇性的。雷达传感器可在患者监测过程中对震颤运动进行连续、客观的评估，它提供了一种非接触、不受光线影响、保护隐私的方法，可通过多普勒效应直接测量震颤的径向运动。由于以往基于雷达的研究缺乏在现实场景中的连续震颤监测，本研究使用频率调制连续波（FMCW）雷达来检测微妙的震颤运动，并估算其频率，以应对临床环境中的大型体动干扰等挑战。17 名健康的参与者在进行震颤评估中常用的三个诊断动作和两个受 PC 环境中常见日常任务启发的活动时，被要求模仿右手的震颤。震颤检测和频率估算是通过适当的雷达信号预处理，然后利用由卷积层和递归层组成的神经网络实现的。参考频率由连接在参与者右手上的惯性测量单元（IMU）获得。交叉验证显示，与参考频率相比，基于雷达的频率估计平均绝对误差（MAE）为 1.47 Hz，区分是否存在震颤的准确率为 90%，这凸显了所提出的方法在未来震颤评估中的巨大潜力。
{"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}
Pub Date : 2024-11-06DOI: 10.1109/TRS.2024.3493037
Yi Li;Weijie Xia;Lingzhi Zhu;Jianjiang Zhou;Yongyan Chu;Wogong Zhang;Jie Zhang
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.
在为短程应用量身定制的分布式雷达网络中，传统的到达方向（DOA）估计往往无法达到最佳性能。近距离目标的存在给雷达回波模型带来了不匹配，对远场（FF）假设的有效性提出了挑战。为了解决这个问题，我们为受近场（NF）效应影响的分布式雷达网络中的 DOA 估测开发了一种误设克拉梅尔-拉奥约束（MCRB）。这一推导有助于理解与误设最大似然估计器的均方误差 (mse) 相关的潜在性能下降。通过综合分析，我们探讨了通常的克拉梅尔-拉奥约束（CRB）与 MCRB 之间的相互作用。此外，我们还对这些界限、目标参数和系统结构之间的关系进行了细致的研究。我们的研究大大提高了雷达在实际场景中的性能，为分布式雷达系统的设计和配置提供了有价值的见解。
{"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}
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.
{"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}
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