Gait recognition is to recognise different individuals based on their faint differences of gait characteristics, which is different from and more challengeable than the recognition of human activities based on relatively bigger differences between different motions. Existing millimetre-wave Multiple Input Multiple Output radar point cloud data contains time-varying three-dimensional spatial positions, velocity, and intensity information. How to enhance the accuracy of gait recognition by effectively utilising the available radar point cloud data has become an attractive research topic in recent years. A velocity-depth-time (VDT) based point cloud construction method for millimetre-wave Multiple Input Multiple Output radar is proposed for gait recognition application, which can not only alleviate the sparsity problem of mmWave point cloud but also make the constructed point cloud to exhibit temporal structural features of micro-motions, and therefore enable the successful application of PointNet++ to mmWave-MIMO point cloud gait recognition. New point clouds are constructed by the proposed method using public gait recognition datasets of 10 and 20 individuals from mmWave-MIMO radar, which are used to conduct gait recognition experiments using PointNet++. The results show that the point clouds constructed based on VDT are more conducive to the gait recognition task. Even using the classic PointNet++ model, which is not specially designed for radar point clouds, high recognition accuracy can be achieved for gait recognition tasks. The recognition accuracies are improved by 11% and 12% in this work for datasets of 10 and 20 individuals, respectively, compared with the 84% and 80% achieved by the traditional method using the same dataset and the same PointNet++ model, while the accuracies are improved by 5% and 12%, respectively, compared with the 90% and 80% achieved by the original dataset thesis method corresponding to 10-individual and 20-individual datasets.
{"title":"Gait-based human recognition based on millimetre wave multiple input multiple output radar point cloud constructed using velocity-depth-time","authors":"Xianxian He, Yunhua Zhang, Xiao Dong","doi":"10.1049/rsn2.12577","DOIUrl":"https://doi.org/10.1049/rsn2.12577","url":null,"abstract":"<p>Gait recognition is to recognise different individuals based on their faint differences of gait characteristics, which is different from and more challengeable than the recognition of human activities based on relatively bigger differences between different motions. Existing millimetre-wave Multiple Input Multiple Output radar point cloud data contains time-varying three-dimensional spatial positions, velocity, and intensity information. How to enhance the accuracy of gait recognition by effectively utilising the available radar point cloud data has become an attractive research topic in recent years. A velocity-depth-time (VDT) based point cloud construction method for millimetre-wave Multiple Input Multiple Output radar is proposed for gait recognition application, which can not only alleviate the sparsity problem of mmWave point cloud but also make the constructed point cloud to exhibit temporal structural features of micro-motions, and therefore enable the successful application of PointNet++ to mmWave-MIMO point cloud gait recognition. New point clouds are constructed by the proposed method using public gait recognition datasets of 10 and 20 individuals from mmWave-MIMO radar, which are used to conduct gait recognition experiments using PointNet++. The results show that the point clouds constructed based on VDT are more conducive to the gait recognition task. Even using the classic PointNet++ model, which is not specially designed for radar point clouds, high recognition accuracy can be achieved for gait recognition tasks. The recognition accuracies are improved by 11% and 12% in this work for datasets of 10 and 20 individuals, respectively, compared with the 84% and 80% achieved by the traditional method using the same dataset and the same PointNet++ model, while the accuracies are improved by 5% and 12%, respectively, compared with the 90% and 80% achieved by the original dataset thesis method corresponding to 10-individual and 20-individual datasets.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuewen Zhou, Fangzheng Zhang, Jiayuan Kong, Shilong Pan
A broadband microwave photonic time division multiplexing (TDM) multiple-input-multiple-output (MIMO) radar is proposed in which photonic frequency quadrupling is adopted to generate broadband radar signals and photonic frequency mixing is implemented for de-chirping processing of radar echoes. By utilising two radio frequency switches to control the signal transmission and reception, TDM-MIMO mechanism is formed using a single microwave photonic radar transceiver. This microwave photonic TDM-MIMO radar not only achieves high range resolution using broadband processing but also enables high angular resolution and forward-looking imaging capability with low system complexity. Besides, a broadband digital beamforming (DBF) method is introduced to solve the broadband beam squint and broadening problems and implement near-field correction. In the experiment, a microwave photonic TDM-MIMO radar with an 8×8 T-shape antenna array is established with a bandwidth of 8 GHz (18–26 GHz) in each channel. The range and angular resolutions are estimated to be ∼2 cm and ∼2°, respectively. Applying the broadband DBF method, high-resolution 3D imaging of small targets is achieved with good focusing of targets and deep suppression of grating lobes and side lobes. Hence, the proposed microwave photonic TDM-MIMO radar with broadband DBF provides a promising solution for high-resolution 3D imaging.
{"title":"High-resolution 3D imaging by microwave photonic time division multiplexing-multiple-input-multiple-output radar with broadband digital beamforming","authors":"Yuewen Zhou, Fangzheng Zhang, Jiayuan Kong, Shilong Pan","doi":"10.1049/rsn2.12590","DOIUrl":"https://doi.org/10.1049/rsn2.12590","url":null,"abstract":"<p>A broadband microwave photonic time division multiplexing (TDM) multiple-input-multiple-output (MIMO) radar is proposed in which photonic frequency quadrupling is adopted to generate broadband radar signals and photonic frequency mixing is implemented for de-chirping processing of radar echoes. By utilising two radio frequency switches to control the signal transmission and reception, TDM-MIMO mechanism is formed using a single microwave photonic radar transceiver. This microwave photonic TDM-MIMO radar not only achieves high range resolution using broadband processing but also enables high angular resolution and forward-looking imaging capability with low system complexity. Besides, a broadband digital beamforming (DBF) method is introduced to solve the broadband beam squint and broadening problems and implement near-field correction. In the experiment, a microwave photonic TDM-MIMO radar with an 8×8 T-shape antenna array is established with a bandwidth of 8 GHz (18–26 GHz) in each channel. The range and angular resolutions are estimated to be ∼2 cm and ∼2°, respectively. Applying the broadband DBF method, high-resolution 3D imaging of small targets is achieved with good focusing of targets and deep suppression of grating lobes and side lobes. Hence, the proposed microwave photonic TDM-MIMO radar with broadband DBF provides a promising solution for high-resolution 3D imaging.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.12590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142170339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Applying the sparse recovery (SR) technique to airborne radar space-time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non-homogeneous and non-stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log-sum penalty to approximate the