Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning
Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a t...
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MDPI AG
2025-05-01
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| author | Ruizhi Liu Zhenhang Qin Xinghui Song Lei Yang Yue Lin Hongtao Xu |
| author_facet | Ruizhi Liu Zhenhang Qin Xinghui Song Lei Yang Yue Lin Hongtao Xu |
| author_sort | Ruizhi Liu |
| collection | DOAJ |
| description | Millimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing. |
| format | Article |
| id | doaj-art-4e91f687a3cc4bfca8195cdd7f189824 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4e91f687a3cc4bfca8195cdd7f1898242025-08-20T03:46:46ZengMDPI AGSensors1424-82202025-05-012511337710.3390/s25113377Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud LearningRuizhi Liu0Zhenhang Qin1Xinghui Song2Lei Yang3Yue Lin4Hongtao Xu5State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, ChinaState Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, ChinaState Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, ChinaICLegend Micro, Shanghai 201203, ChinaICLegend Micro, Suzhou 215134, ChinaState Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, ChinaMillimeter-wave (mmWave) radar is increasingly used in smart environments for human detection due to its rich sensing capabilities and sensitivity to subtle movements. However, indoor multipath propagation causes severe ghost target issues, reducing radar reliability. To address this, we propose a trajectory-based ghost suppression method that integrates multi-target tracking with point cloud deep learning. Our approach consists of four key steps: (1) point cloud pre-segmentation, (2) inter-frame trajectory tracking, (3) trajectory feature aggregation, and (4) feature broadcasting, effectively combining spatiotemporal information with point-level features. Experiments on an indoor dataset demonstrate its superior performance compared to existing methods, achieving 93.5% accuracy and 98.2% AUROC. Ablation studies demonstrate the importance of each component, particularly the complementary benefits of pre-segmentation and trajectory processing.https://www.mdpi.com/1424-8220/25/11/3377millimeter-wave radarghost suppressionmulti-target trackingpoint cloud segmentationmultipath |
| spellingShingle | Ruizhi Liu Zhenhang Qin Xinghui Song Lei Yang Yue Lin Hongtao Xu Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning Sensors millimeter-wave radar ghost suppression multi-target tracking point cloud segmentation multipath |
| title | Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning |
| title_full | Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning |
| title_fullStr | Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning |
| title_full_unstemmed | Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning |
| title_short | Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning |
| title_sort | indoor mmwave radar ghost suppression trajectory guided spatiotemporal point cloud learning |
| topic | millimeter-wave radar ghost suppression multi-target tracking point cloud segmentation multipath |
| url | https://www.mdpi.com/1424-8220/25/11/3377 |
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