$\textbf{EchMo}$: End-to-End Radar Echo Segmentation

This paper presents an End-to-End learnable radar semantic segmentation network using I/Q echoes as input for Doppler radars, named as $\textbf{EchMo} (\text{\textbf{ech}o-in-\textbf{m}ask-\textbf{o}ut})$. EchMo reinterprets the steps of conventional radar signal processing (RSP) in a deeply learnable manner, including pulse compression (traditionally implemented via matched filtering), moving target indication (MTI), coherent integration (CI), and target detection. Ultimately, it achieves a single compact learnable deep model that goes from I/Q echoes to target semantic segmentation in one step, delivering a streamlined differentiable computational graph and substantial efficiency gains. Experimental results show that, compared to conventional pipeline or deep models based on intermediate radio-frequency (RF) representation, EchMo achieves better results and offers a more interpretable foundation. EchMo is the first pulse-Doppler I/Q echo-based end-to-end radar semantic segmentation deep architecture, which is a milestone for the fields of RSP, remote sensing, and modern deep learning. The code link for review: https://anonymous.4open.science/anonymize/EchMo-6877.