Clutter suppression for ground penetrating radar echo signal based on layer division processing

Abstract

To improve the accuracy of underground target identification, clutter must be efficiently suppressed in ground-penetrating radar(GPR) echo signal. Classic methods such as Robust Principal Component Analysis (RPCA) and Factor Group-Sparse Regularization (FGSR) have been widely applied in GPR signal processing. RPCA separates background and target signals based on low-rank and sparse decomposition. FGSR removes large-scale surface clutter using morphological operations. However, both methods face limitations under non-uniform subsurface conditions, where the background and target signals are highly complex and overlapping. Based on the characteristics of echo signal, a clutter suppression method is proposed, namely adaptive layer division processing combined with two-dimensional wavelet transform. A Peplinski's heterogeneous soil model containing underground targets is constructed in gprMax to evaluate the effectiveness and applicability of the proposed method. By analyzing the statistical properties of kurtosis and skewness, adaptive layer division processing is applied to preliminarily separate the direct wave, background clutter, and target echo reflection signals. The two-dimensional wavelet transform is then applied to suppress clutter in the target signal layer, and the final image is reconstructed. Simulation results show that adaptive layer division processing enhances the clutter suppression performance of conventional denoising methods. The proposed method, integrating two-dimensional wavelet transform, demonstrates superior clutter suppression performance, where the signal-to-clutter ratio (SCR) is improved to 15.45 dB, the image entropy is reduced to 2.51, the improvement factor (IF) achieves a positive value of 1.63 dB, and the peak signal-to-noise ratio (PSNR) rises to 27.63 dB. The proposed method provides an effective approach for processing GPR echo signals under non-uniform subsurface conditions.