Real-Time Through-Wall Detection of Stationary Human Targets Using Micro-Doppler and Adaptive Interference Suppression

Through-wall radar (TWR) has significant potential in security surveillance and post-disaster search and rescue. However, in complex environments, weak human echoes are often masked by strong clutter and multi-source interference, severely degrading detection performance. This paper presents a novel detection method that integrates micro-Doppler analysis with adaptive interference suppression for stationary human targets. For narrowband interference, an improved vector mean-canceling algorithm (VMCA) combined with range–time two-dimensional filtering is introduced to achieve adaptive clutter and interference suppression. For broadband interference, a frequency-domain adaptive filtering technique is developed that leverages the complex-baseband architecture to construct a negative-frequency reference channel from the conjugate correlation between positive and negative spectra, thereby canceling broadband noise and pushing the measurement sensitivity of TWR towards its hardware noise limit. To enable real-time deployment, an efficient parallel pipelined processing architecture is implemented on a Xilinx XC7Z035 field-programmable gate array (FPGA), with total power consumption below 3W and a core processing delay of approximately 10ms. Experimental results on a through-wall radar testbed demonstrate that, compared with representative recent matrix decomposition methods, the proposed framework provides improved interference suppression and more stable micro-Doppler energy focusing across diverse target postures, leading to consistently higher detection performance in cluttered scenarios. These results confirm the effectiveness and practical engineering feasibility of the proposed framework in complex through-wall environments.