Motion-Adaptive Through-Wall Moving Target Enhancement Based on Energy-Guided Competitive Fusion

Reliable through-wall human imaging is challenging because strong wall clutter and motion-dependent target responses can degrade target preservation, particularly under low signal-to-noise ratio (SNR) conditions. This paper proposes a motion-adaptive through-wall moving-target enhancement framework based on energy-guided competitive fusion. After wall clutter suppression and wall-compensated imaging, each pixelwise complex slow-time sequence is decomposed into complementary zero-Doppler and nonzero-Doppler response branches. Kapur maximum-entropy segmentation is then used to construct a common candidate support, from which a data-derived three-branch energy reference distribution is obtained for the zero-Doppler, nonzero-Doppler, and virtual-background hypotheses. The resulting reference-weighted Softmax fusion is formulated as the closed-form solution of a Kullback–Leibler (KL)-regularized optimization problem. Simulations and measured through-wall experiments show that the two decomposed branches provide different relative responses to targets with different motion states, while the proposed fusion improves overall target-background separation.