A Real-Time Beam Steering and Accurate Vital Sign Estimation Method in an Indoor Environment

An accurate heart rate (HR) estimation using a radar sensor is challenging in real-life situations due to frequent changes in a person’s sitting position and posture. Furthermore, radar signal is affected by the presence of clutter noise, the harmonics associated with respiration (RR), and the movements of the body. To address these challenges, this article proposes a real-time beam steering algorithm and a signal processing technique based on Resonance Sparse Spectrum Decomposition (RSSD). Our beam steering method dynamically calculates the range–angle values of the target during the scanning phase and determines the target’s position at the beginning of each measurement cycle. This allows the beam-steered signal to be directed toward the individual, thereby improving the signal-to-noise ratio (SNR). We present a novel signal processing method based on RSSD that leverages sub-band energy distribution to optimize the quality factor (Q) and the subsequent extraction of HR using harmonics. The Q factor defines the resonance property of an oscillatory signal, and hence, signal components with similar center frequency bands but with a different quality factor, Q, can be separated and sparsely represented. The RSSD-based algorithm mitigates the effects of clutter and random body motion from the phase signal, which significantly enhances HR estimation accuracy. Comprehensive experiments performed under various realistic conditions demonstrate that the HR accuracy remains consistently high at 98.72% within a 4 m range across all azimuth angles.