Radar Human Motion Classification Using Multi-Antenna System

TL;DR

This paper presents a radar-based method utilizing multi-antenna systems and time-frequency analysis to classify human motions, demonstrating effective separation and recognition of walking activities in real-time indoor environments.

Contribution

It introduces a novel multi-antenna radar approach with TF analysis and machine learning for improved human motion classification and separation.

Findings

Effective classification of walking motions with micro-Doppler signatures.

Successful separation of two persons walking in opposite directions.

Real-time data collection at 77 GHz with four antennas.

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.