Because of the limitations of traditional Principal Component Analysis (PCA) in clutter reduction, an improved PCA subspace method is proposed based on the 2D wavelet transform. Moreover, the combination of the improved subspace method and adaptive filtering ensures the signal fidelity and learning adaptability of adaptive filtering. Then, an adaptive clutter reduction algorithm based on wavelet transform and PCA, as well as adaptive filtering, is proposed. The experimental results suggest that the proposed method improves the signal to clutter ratio and target image definition.
