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Orthogonal Trajectories01:26

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Indoor mmWave Radar Ghost Suppression: Trajectory-Guided Spatiotemporal Point Cloud Learning.

Ruizhi Liu1, Zhenhang Qin1, Xinghui Song1

  • 1State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China.

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Summary

This study introduces a novel trajectory-based ghost suppression method for millimeter-wave (mmWave) radar human detection. The approach effectively reduces ghost targets caused by indoor multipath propagation, enhancing radar system reliability.

Keywords:
ghost suppressionmillimeter-wave radarmulti-target trackingmultipathpoint cloud segmentation

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Area of Science:

  • Radar Sensing
  • Artificial Intelligence
  • Smart Environments

Background:

  • Millimeter-wave (mmWave) radar offers advanced human detection in smart environments.
  • Indoor multipath propagation severely impacts mmWave radar reliability by creating ghost targets.

Purpose of the Study:

  • To develop a robust ghost suppression method for mmWave radar systems.
  • To enhance the reliability of human detection in indoor environments.

Main Methods:

  • A trajectory-based ghost suppression technique integrating multi-target tracking and deep learning on point clouds.
  • Key steps include point cloud pre-segmentation, inter-frame trajectory tracking, trajectory feature aggregation, and feature broadcasting.
  • Combines spatiotemporal information with point-level features for improved accuracy.

Main Results:

  • Achieved 93.5% accuracy and 98.2% AUROC on an indoor dataset.
  • Demonstrated superior performance over existing ghost suppression methods.
  • Ablation studies confirmed the effectiveness of individual components, especially pre-segmentation and trajectory processing.

Conclusions:

  • The proposed trajectory-based method significantly improves mmWave radar performance for indoor human detection.
  • Effective suppression of ghost targets enhances the overall reliability and accuracy of radar systems.
  • The integration of trajectory tracking and deep learning offers a promising direction for future radar sensing research.