Volume 13 Issue 4
Aug.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(4): 791-806
WANG Yingfu, YIN Jiapeng, LU Zhonghao, et al. Analysis of the influence of distributed interrupted-sampling repeating signals on airborne interferometer parameter measurements[J]. Journal of Radars, 2024, 13(5): 1037–1048. doi: 10.12000/JR24090
Citation: CAI Xiang, WEI Shunjun, WEN Yanbo, et al. Precise reconstruction method for hidden targets based on non-line-of-sight radar 3D imaging[J]. Journal of Radars, 2024, 13(4): 791–806. doi: 10.12000/JR24060
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Precise Reconstruction Method for Hidden Targets Based on Non-line-of-sight Radar 3D Imaging

DOI: 10.12000/JR24060 CSTR: 32380.14.JR24060

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (62271108)
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  • Corresponding author: WEI Shunjun, weishunjun@uestc.edu.cn
  • Received Date: 2024-04-03
  • Rev Recd Date: 2024-05-19
    • Available Online: 2024-05-25
  • Publish Date: 2024-06-25
    Abstract
  • Non-Line-Of-Sight (NLOS) 3D imaging radar is an emerging technology that utilizes multipath scattering echoes to detect hidden targets. However, this technology faces challenges such as the separation of multipath echoes, reduction of aperture occlusion, and phase errors of reflective surfaces, which hinder the high-precision imaging of hidden targets when using traditional Line-Of-Sight (LOS) radar imaging methods. To address these challenges, this paper proposes a precise imaging method for NLOS hidden targets based on Sparse Iterative Reconstruction (NSIR). In this method, we first establish a multipath signal model for NLOS millimeter-wave 3D imaging radar. By exploiting the characteristics of LOS/NLOS echoes, we extract the multipath echoes from hidden targets using a model-driven approach to realize the separation of LOS/NLOS echo signals. Second, we formulate a total variation multiconstraint optimization problem for reconstructing hidden targets, integrating multipath reflective surface phase errors. Using the split Bregman Total Variation (TV) regularization operator and the phase error estimation criterion based on the minimum mean square error, we jointly solve the multiconstraint optimization problem. This approach facilitates precise imaging and contour reconstruction of NLOS targets. Finally, we construct a planar scanning 3D imaging radar experimental platform and conduct experimental verification of targets such as knives and iron racks in a corner NLOS scenario. Results validate the capability of NLOS millimeter-wave 3D imaging radar in detecting hidden targets and the effectiveness of the method proposed in this paper.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 9.4 %其他: 9.4 %其他: 1.0 %其他: 1.0 %Central District: 0.1 %Central District: 0.1 %China: 0.2 %China: 0.2 %Ecole-Valentin: 0.2 %Ecole-Valentin: 0.2 %Falls Church: 0.3 %Falls Church: 0.3 %Herndon: 0.1 %Herndon: 0.1 %Russian Federation: 0.1 %Russian Federation: 0.1 %上海: 3.0 %上海: 3.0 %东莞: 0.1 %东莞: 0.1 %伦敦: 0.1 %伦敦: 0.1 %佛山: 0.3 %佛山: 0.3 %六安: 0.1 %六安: 0.1 %兰州: 0.2 %兰州: 0.2 %内江: 0.1 %内江: 0.1 %列克星敦: 0.1 %列克星敦: 0.1 %加利福尼亚州: 0.4 %加利福尼亚州: 0.4 %北京: 18.2 %北京: 18.2 %十堰: 0.2 %十堰: 0.2 %南京: 3.9 %南京: 3.9 %南宁: 0.1 %南宁: 0.1 %南昌: 0.6 %南昌: 0.6 %南通: 0.1 %南通: 0.1 %印多尔: 0.1 %印多尔: 0.1 %厦门: 0.3 %厦门: 0.3 %双鸭山: 0.1 %双鸭山: 0.1 %台北: 0.2 %台北: 0.2 %台州: 0.2 %台州: 0.2 %合肥: 0.9 %合肥: 0.9 %吉安: 0.1 %吉安: 0.1 %呼和浩特: 0.2 %呼和浩特: 0.2 %哈尔滨: 0.5 %哈尔滨: 0.5 %哥伦布: 0.2 %哥伦布: 0.2 %嘉兴: 0.8 %嘉兴: 0.8 %圣克拉拉: 0.1 %圣克拉拉: 0.1 %圣安东尼奥: 0.1 %圣安东尼奥: 0.1 %大理: 0.3 %大理: 0.3 %大连: 0.2 %大连: 0.2 %大阪: 0.1 %大阪: 0.1 %天津: 0.6 %天津: 0.6 %太原: 0.3 %太原: 0.3 %威海: 0.3 %威海: 0.3 %宁波: 0.1 %宁波: 0.1 %安康: 0.3 %安康: 0.3 %安顺: 0.1 %安顺: 0.1 %宜春: 0.1 %宜春: 0.1 %宣城: 0.3 %宣城: 0.3 %宿州: 0.1 %宿州: 0.1 %常州: 0.5 %常州: 0.5 %常德: 0.1 %常德: 0.1 %广安: 0.1 %广安: 0.1 %广州: 1.2 %广州: 1.2 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %延安: 0.1 %延安: 0.1 %开封: 0.8 %开封: 0.8 %张家口: 1.0 %张家口: 1.0 %张家界: 0.2 %张家界: 0.2 %德里: 0.1 %德里: 0.1 %德黑兰: 0.1 %德黑兰: 0.1 %慕尼黑: 0.1 %慕尼黑: 0.1 %成都: 2.9 %成都: 2.9 %扬州: 0.4 %扬州: 0.4 %揭阳: 0.1 %揭阳: 0.1 %新乡: 0.1 %新乡: 0.1 %新余: 0.1 %新余: 0.1 %无锡: 0.1 %无锡: 0.1 %昆明: 1.9 %昆明: 1.9 %朝阳: 0.2 %朝阳: 0.2 %本溪: 0.1 %本溪: 0.1 %杭州: 2.2 %杭州: 2.2 %格林维尔: 0.1 %格林维尔: 0.1 %武汉: 0.6 %武汉: 0.6 %永州: 0.1 %永州: 0.1 %汕头: 0.4 %汕头: 0.4 %江门: 0.1 %江门: 0.1 %沈阳: 0.2 %沈阳: 0.2 %沧州: 0.1 %沧州: 0.1 %河源: 0.2 %河源: 0.2 %洛杉矶: 0.1 %洛杉矶: 0.1 %洛阳: 0.2 %洛阳: 0.2 %济南: 0.3 %济南: 0.3 %海口: 0.2 %海口: 0.2 %淄博: 0.1 %淄博: 0.1 %淮南: 0.1 %淮南: 0.1 %深圳: 2.2 %深圳: 2.2 %温州: 0.5 %温州: 0.5 %湖州: 0.1 %湖州: 0.1 %湘潭: 0.1 %湘潭: 0.1 %漯河: 0.7 %漯河: 0.7 %潍坊: 0.1 %潍坊: 0.1 %烟台: 0.1 %烟台: 0.1 %珠海: 0.4 %珠海: 0.4 %白城: 0.1 %白城: 0.1 %百色: 0.2 %百色: 0.2 %石家庄: 0.3 %石家庄: 0.3 %福州: 0.2 %福州: 0.2 %纽约: 0.6 %纽约: 0.6 %绵阳: 1.4 %绵阳: 1.4 %罗马: 0.2 %罗马: 0.2 %芒廷维尤: 10.5 %芒廷维尤: 10.5 %芝加哥: 1.1 %芝加哥: 1.1 %苏州: 0.5 %苏州: 0.5 %莫斯科: 0.3 %莫斯科: 0.3 %营口: 0.1 %营口: 0.1 %衡水: 0.5 %衡水: 0.5 %衡阳: 0.2 %衡阳: 0.2 %衢州: 0.2 %衢州: 0.2 %襄阳: 0.1 %襄阳: 0.1 %西宁: 3.2 %西宁: 3.2 %西安: 3.8 %西安: 3.8 %诺沃克: 5.5 %诺沃克: 5.5 %贵阳: 0.3 %贵阳: 0.3 %赣州: 0.1 %赣州: 0.1 %运城: 0.3 %运城: 0.3 %通辽: 0.1 %通辽: 0.1 %遵义: 0.1 %遵义: 0.1 %邯郸: 0.2 %邯郸: 0.2 %邵阳: 0.2 %邵阳: 0.2 %郑州: 0.3 %郑州: 0.3 %重庆: 1.1 %重庆: 1.1 %金昌: 0.1 %金昌: 0.1 %长春: 0.2 %长春: 0.2 %长沙: 2.2 %长沙: 2.2 %阜新: 0.1 %阜新: 0.1 %阿什本: 0.2 %阿什本: 0.2 %阿姆斯特丹: 0.1 %阿姆斯特丹: 0.1 %陇南: 0.1 %陇南: 0.1 %陵水: 0.1 %陵水: 0.1 %青岛: 0.8 %青岛: 0.8 %首尔特别: 0.2 %首尔特别: 0.2 %香港: 0.2 %香港: 0.2 %马尔默: 0.1 %马尔默: 0.1 %马尼拉: 0.2 %马尼拉: 0.2 %驻马店: 0.1 %驻马店: 0.1 %黄石: 0.1 %黄石: 0.1 %齐齐哈尔: 0.5 %齐齐哈尔: 0.5 %其他其他Central DistrictChinaEcole-ValentinFalls ChurchHerndonRussian Federation上海东莞伦敦佛山六安兰州内江列克星敦加利福尼亚州北京十堰南京南宁南昌南通印多尔厦门双鸭山台北台州合肥吉安呼和浩特哈尔滨哥伦布嘉兴圣克拉拉圣安东尼奥大理大连大阪天津太原威海宁波安康安顺宜春宣城宿州常州常德广安广州库比蒂诺延安开封张家口张家界德里德黑兰慕尼黑成都扬州揭阳新乡新余无锡昆明朝阳本溪杭州格林维尔武汉永州汕头江门沈阳沧州河源洛杉矶洛阳济南海口淄博淮南深圳温州湖州湘潭漯河潍坊烟台珠海白城百色石家庄福州纽约绵阳罗马芒廷维尤芝加哥苏州莫斯科营口衡水衡阳衢州襄阳西宁西安诺沃克贵阳赣州运城通辽遵义邯郸邵阳郑州重庆金昌长春长沙阜新阿什本阿姆斯特丹陇南陵水青岛首尔特别香港马尔默马尼拉驻马店黄石齐齐哈尔

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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