Radar human motion classification using multi-antenna system

Patrick A. Schooley; Syed A. Hamza

doi:10.1117/12.2588700

12 April 2021 Radar human motion classification using multi-antenna system

Patrick A. Schooley, Syed A. Hamza

Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 1173005 (2021) https://doi.org/10.1117/12.2588700
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

This paper considers human activity classification for an indoor radar system. Human motions generate nonstationary radar returns which represent Doppler and micro-Doppler signals. The time-frequency (TF) analysis of micro-Doppler signals can discern subtle variations on the motion by precisely revealing velocity components of various moving body parts. We consider radar for activity monitoring using TF-based machine learning approach exploiting both temporal and spatial degrees of freedom. The proposed approach captures different human motion representations more vividly in joint-variable data domains achieved through beamforming at the receiver. The radar data is collected using real time measurements at 77 GHz using four receive antennas, and subsequently micro-Doppler signatures are analyzed through machine learning algorithm for classifications of human walking motions. We present the performance of the proposed multi antenna approach in separating and classifying two closely walking persons moving in opposite directions.

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Citation Download Citation

Patrick A. Schooley and Syed A. Hamza "Radar human motion classification using multi-antenna system", Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 1173005 (12 April 2021); https://doi.org/10.1117/12.2588700

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