Spatiotemporal Weighted Micro-Doppler Spectrum Design for Soft Synchronization FMCW Radar

Li, P.-C.; Wang, Tao; He, Zhiwei; Gao, Mingyu; Yang, Yuxiang; Huang, Jiye

doi:10.1109/tim.2023.3298408

Cited by 1 publication

(1 citation statement)

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“…Yuan He and Francesco Fioranelli et al suggested an instance-based transfer learning approach for recognizing human motion with radar using limited training data [38]. Peng Li et al developed a novel spatiotemporal weighted MD spectrum that accounts for the primary and secondary importance information in the reduced dimension space and the temporal information of the sequences [39].…”

Section: The Micro-doppler Of the Human Posturementioning

confidence: 99%

SCRP-Radar: Space-Aware Coordinate Representation for Human Pose Estimation Based on SISO UWB Radar

Zhou,

Jin,

Dai

et al. 2024

Remote Sensing

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Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies.

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Section: The Micro-doppler Of the Human Posturementioning

confidence: 99%