PathClean: Breaking the Multipath Barrier for High-Fidelity mmWave Human Point Clouds

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Abstract
High-fidelity mmWave human point clouds are pivotal for applications like fall detection, people counting, and multi-person pose estimation. However, commercial radars are often hindered by multipath interference in cluttered indoor environments, which introduces ghost targets and distorts human structures. Prior attempts at mitigation either rely on temporal continuity, environmental priors, or data-hungry learning models, all of which suffer from latency, fragility, or high overhead. We present PathClean, the first system to eliminate multipath interference directly during point cloud generation instead of post-processing. PathClean leverages the inherent Angle of Arrival (AoA)-Angle of Departure (AoD) consistency of direct paths to instantly suppress multipath, while addressing the challenges of dense matching ambiguity, hardware imperfections, and real-time constraints. Implemented on commodity radars, PathClean removes 98.9% of multipath points, while retaining 97.6% of true human points. Extensive experiments across seven environments and three case studies—fall detection, people counting, and multi-person pose estimation—demonstrate that PathClean is robust, scalable, and practical for real-world deployment.
Funding Information
  • Beijing Natural Science Foundation (No. IS26037)

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