An Adaptive SVD-Based Approach to Clutter Suppression for Slow-Moving Targets

The presence of strong clutter remains a critical challenge for radar system target detection. Traditional clutter suppression techniques such as Doppler-based filters often fail to extract low-velocity targets from clutter. To address this limitation, this paper proposes an adaptive singular value decomposition (A-SVD) method utilizing support vector machines (SVM). The proposed approach leverages the augmented implicitly restarted Lanczos bidiagonalization (AIRLB) algorithm to decompose echo matrices into different subspaces, which are then characterized in relation to Doppler frequency, energy, and correlation. These features are employed to classify the clutter subspaces using an SVM classifier, which solves the problem of selecting the SVD threshold. The clutter subspaces are suppressed by zeroing out corresponding singular values, and the matrix is then recomposed by the rest of the subspaces to recover the echo. Experiments on simulated and real datasets show that the proposed method achieves an average improvement factor (IF) above 40 dB and reduces runtime by over 85% in most scenarios.