Deep Learning for Velocity and Range Estimation of Drones in Urban Occlusion Environments

This paper investigates the challenge of radar echo loss caused by urban occlusions in low-altitude aerial drone surveillance. We propose a deep learning framework, built on an enhanced U-Net, to restore radar signals and improve joint velocity and range estimation. A Slow-Fast Time Echo Matrix (SFTEM) is constructed to encode temporal and phase features, serving as the input to the network. Matched filtering is first applied to form the SFTEM as the network input, followed by Doppler FFT on the restored SFTEM for range–velocity estimation. To address severe pulse loss, the model incorporates axial attention and residual connections, while a Partial-Edge Preserved Pulse Loss (PEPPL) model is introduced to simulate realistic urban blockages. Experimental results demonstrate a mean range error of about$ 2.08m$, which is well below the range resolution ($ 5--15m$), and a velocity error of$0.749\,\mathrm{m/s}$, even under conditions with over 90% central pulse loss, highlighting the robustness and effectiveness of the proposed approach for aerial drone sensing in complex urban environments.