Deep CNN-Based U-Net Framework for Accurate Human Detection in Complex Through-the-Wall Scenarios

Le résumé fourni par la source

Through-the-wall radar imaging (TWRI) enables critical tasks such as search and rescue, surveillance, and health monitoring. However, strong wall reflections often obscure weak human signatures. This work presents a simulation-driven framework for detecting stationary and breathing individuals at low frequencies behind walls. Synthetic radar datasets were generated in MATLAB, modeling multiple wall types and clutter conditions. A convolutional neural network (CNN)-based U-Net was developed, trained, and validated to suppress wall clutter and enhance target visibility. The model effectively distinguishes human responses from dominant wall echoes, yielding over 30 dB improvement in signal-to-clutter ratio. Tests on synthetic and emulated radar data further confirm its ability to recover weak target echoes while maintaining real-time throughput within FPGA power limits. Results demonstrate the robustness and efficiency of the proposed deep learning approach, establishing a strong foundation for embedded implementation and practical field deployment in complex through-wall scenarios.

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