Trajectory smoothness evaluation and occlusion compensation for mmwave radar point cloud enhancement method

By using trajectory smoothness evaluation and occlusion compensation methods, ghosting is eliminated by TSI and SNR verification, and virtual points are generated by Kalman filtering to compensate for occlusion. This solves the problems of distinguishing ghosting from real targets and trajectory breakage caused by occlusion, achieving high-precision trajectory tracking and continuity, while reducing cost and privacy risks.

CN122656911AActive Publication Date: 2026-08-28JILIN UNIVERSITY
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Patent Information

Application Number
CN202611144710.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-07-30
Publication Date
2026-08-28
Estimated Expiration
2046-07-30

AI Technical Summary

Technical Problem

Existing technologies cannot effectively distinguish between ghosts and real targets, and the trajectory breakage caused by occlusion leads to errors in people counting and behavior recognition. Furthermore, existing methods suffer from high hardware costs, privacy risks, and high data annotation costs.

Method used

By using trajectory smoothness evaluation and occlusion compensation methods, the trajectory smoothness index (TSI) is calculated using inter-frame correlation and human motion physical constraint parameters. It is then combined with signal-to-noise ratio (SNR) for two-dimensional verification to eliminate false targets. Furthermore, when occluded, virtual points are generated through Kalman filtering to compensate for trajectory loss.

Benefits of technology

It achieves high-precision false target removal and continuous trajectory tracking, with a real target recognition rate of 98.3% and a trajectory deviation reduction rate of 78.5%, avoiding additional hardware costs and privacy leakage risks.

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Abstract

The application discloses a millimeter wave radar point cloud enhancement method for trajectory smoothness evaluation and occlusion compensation, and belongs to the technical field of radar signal processing. The method performs clustering and inter-frame association on original radar point cloud data, and constructs a target motion trajectory. A trajectory smoothness index is calculated. From three dimensions of velocity variation, acceleration variation and motion direction variation, product type aggregation evaluation is performed based on human motion physical constraint parameters, and two-dimensional verification is performed in combination with a signal-to-noise ratio. For real targets and suspicious targets, when occlusion occurs, virtual points are generated frame by frame based on Kalman filter prediction steps, and are dynamically calculated by an environmental complexity factor and adaptively adjusted according to the occlusion time and target speed. A position standard deviation is used as a reliability criterion, and reliable virtual points are inserted into the trajectory to compensate for data loss. The application can effectively eliminate ghost false targets and maintain trajectory continuity when occlusion occurs.
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