A Nonlinear Spectral Approach for Radar-Based Heartbeat Estimation via Autocorrelation of Higher Harmonics

This study presents a nonlinear signal processing method for accurate radar-based heartbeat interval estimation by exploiting the periodicity of higher order harmonics inherent in heartbeat signals. Unlike conventional approaches that employ selective frequency filtering or track individual harmonics, the proposed method enhances the global periodic structure of the spectrum via nonlinear correlation processing. Specifically, smoothing and second-derivative operations are first applied to the radar displacement signal to suppress noise and accentuate higher order heartbeat harmonics. Rather than isolating specific frequency components, we compute localized autocorrelations of the Fourier spectrum around the harmonic frequencies. The incoherent summation of these autocorrelations yields a pseudo-spectrum in which the fundamental heartbeat periodicity is distinctly emphasized. This nonlinear approach mitigates the effects of respiratory harmonics and noise, enabling robust interbeat interval estimation. Experiments with radar measurements from five participants demonstrate that the proposed method reduces absolute error (AE) by 34% and root-mean-square error (RMSE) by 35% and improves the false detection rate (FDR) by 1.37 points relative to conventional techniques.