Through-wall radar imaging (TWRI) is vital for detecting concealed objects in civil and military scenarios. Its performance, however, is often hindered by clutter signals, such as antenna coupling, wall reflections, and environmental noise, which degrade imaging quality. This paper proposes a novel clutter suppression framework utilizing multiple-input multiple-output (MIMO) radar. The approach employs subarray configurations to reduce clutter and enhance target features. Techniques such as singular value thresholding (SVT) and robust principal component analysis (RPCA) effectively mitigate reflective wall clutter in the early stages. To further refine image quality, coherence factor (CF) weighting intensifies target signals while suppressing ghost artifacts. Additionally, a discrete wavelet transform (DWT)-based fusion strategy is introduced to address residual clutter. Experimental results validate that the proposed method significantly improves target detection accuracy in TWRI scenarios.