Zhenhang Qin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhenhang Qin has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Hongtao Xu
Fudan University
1 shared publication - Lei Yang
ICLegend Micro, Shanghai 201203, China
1 shared publication - Ruizhi Liu
State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
1 shared publication - Xinghui Song
State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
1 shared publication - Yue Lin
ICLegend Micro, Shanghai, China
1 shared publication
Latest Publications
- Open Access
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 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.
Open AccessIndoor 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 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.