Micro-Doppler Energy-Based Robust Multi-Target Vital Signs Monitoring Using 77-GHz FMCW Radar with Spatiotemporal Adaptive Processing

TL;DR

This paper introduces a micro-Doppler energy-based framework for robust multi-target vital signs monitoring using 77-GHz FMCW radar, outperforming phase-based methods especially in noisy and dynamic environments.

Contribution

It develops a novel micro-Doppler energy extraction method combined with adaptive processing techniques, enhancing robustness and resolution in multi-target vital signs detection.

Findings

Accurately detects and separates vital signs of up to four targets within 5 meters.

Achieves mean absolute errors of 1.2 bpm for respiration and 2.3 bpm for heart rate.

Demonstrates superior performance over traditional phase-based methods in noisy, multi-target scenarios.

Abstract

This paper presents a novel micro-Doppler energy-based framework for robust multi-target vital signs monitoring using 77-GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional phase-based methods that are susceptible to environmental noise, random body movements, and stringent calibration requirements, our approach exploits the energy variations in radar returns induced by cardiopulmonary activities. The proposed system integrates a comprehensive processing pipeline including space-time adaptive processing (STAP) for target detection and tracking, MUSIC algorithm for high-resolution angle estimation, and an innovative adaptive spectral filtering technique for vital signs extraction. We establish a rigorous mathematical framework that formalizes the relationship between micro-Doppler energy variations and physiological activities, enabling robust separation of closely spaced targets. The key innovation lies in the micro-Doppler energy extraction methodology that provides inherent robustness to phase noise and motion artifacts. Experimental results using millimeter-wave radar datasets demonstrate that the system can accurately detect and separate vital signs of up to four targets within \SI{5}{\meter} range, achieving mean absolute errors of \SI{1.2}beats per minute and \SI{2.3} beats per minute for respiration and heart rates, respectively. The proposed approach demonstrates superior performance compared to traditional phase-based methods, particularly in challenging multi-target scenarios with environmental noise and subject movement.