Through-Wall Radar Detection of Weak Vital Signs Based on Joint Space–Time–Frequency Enhancement

Abstract

To address the challenge of effectively detecting weak vital signs in through-wall radar (TWR) applications under low signal-to-noise ratio (SNR) conditions, this article proposes a detection algorithm based on joint space–time–frequency enhancement. Radar echoes first undergo pulse compression and preprocessing. In the space–time domain, adaptive noise filtering is performed on cross-channel signals based on maximum covariance analysis (MCA), followed by a correlation-weighted accumulation to realize joint space–time enhancement. Subsequently, coherent integration is performed via fast Fourier transform (FFT) to transform the signal into the frequency domain. On this basis, the harmonic product spectrum (HPS) technique is then employed to perform multiscale spectral fusion on the range-Doppler (RD) matrix, effectively enhancing periodic respiration signals while significantly suppressing random noise. Next, exploiting the spatial distribution, the enhanced multichannel spectra are fused to construct a cross-channel correlation matrix, generating a vital sign feature map that benefits from the joint space–time–frequency enhancement. Following this, by combining constant false alarm rate (CFAR) detection with an energy criterion, the algorithm achieves automatic target identification and range pairing. Finally, the 2-D spatial coordinates of the targets are resolved using the elliptical intersection method. Experimental results demonstrate that by fully exploiting multidimensional space–time–frequency features, the proposed method effectively overcomes the difficulty of detecting and localizing weak vital signs under low SNR conditions. The method significantly enhances the correlation of target echoes, and its detection accuracy under low SNR outperforms that of the referenced methods, enabling robust detection.