Through-the-Wall Radar Target Detection Algorithm Based on Cross-Correlation Adaptive Robust Principal Component Analysis

In through-the-wall detection scenarios with low signal-to-noise ratio (SNR) and strong clutter, existing target detection methods generally suffer from inaccuracies, poor real-time performance, and the limitation of detecting only moving or stationary targets. To address these challenges, this paper proposes a through-the-wall radar (TWR) target detection method based on cross-correlation adaptive robust principal component analysis (CCARPCA) capable of simultaneously detecting multiple moving and stationary targets. First, pulse compression is applied to original echo signals using the inverse fast Fourier transform, resulting in high-resolution one-dimensional range profiles. Second, the principal component analysis algorithm suppresses strong clutter interferences, thereby improving the SNR. Next, the back projection algorithm is employed for multi-channel coherent imaging, enabling the extraction of 2-dimensional information and enhancing the sparsity of cross-correlation data. Lastly, considering the drawbacks of the robust principal component analysis (RPCA), such as long detection time and poor robustness, this paper introduces the cross-correlation coefficient and proposes the CCARPCA algorithm, which completely separates the target from the background noise. The experimental results based on a series of simulated and measured data demonstrate the effectiveness of the proposed method in detecting both moving and stationary targets behind walls. Compared to generalized likelihood ratio test, constant false alarm rate, and RPCA, our method achieves a substantial improvement of over 16.4% in detection accuracy based on measured data while maintaining real-time detection capability. Additionally, its detection performance is less sensitive to changes in initial parameters, indicating its superior robustness.