High-Speed Range-Spread Target Detection via Joint Velocity-Robust Keystone Transform and RSTDNet

Detecting high-speed range-spread targets poses significant challenges due to inter-pulse Range Migration (RM) and target energy dispersion across multiple range bins. This paper addresses these challenges by proposing a joint Velocity-Robust Keystone Transform and Range-Spread Target Detection Network (VRKT-RSTDNet) algorithm. The VRKT algorithm is proposed to mitigate inter-pulse energy loss caused by RM. It maps ambiguous and half-ambiguous velocities into the unambiguous region through corresponding velocity compensation, performs slow-time correction and coherent integration to accumulate target energy, and reorganizes the Doppler frequency spectrum to obtain the final result. The VRKT algorithm achieves accurate RM correction and robust energy accumulation across arbitrary target velocities. For range-spread target detection, a deep neural network named RSTDNet is proposed, which integrates multi-scale convolutions and residual connections for multi-scale hierarchical feature extraction. The training dataset is generated based on the electromagnetic scattering model of a typical target, enabling RSTDNet to learn priors for effective detection across varying velocities, attitudes, and Signal-to-Noise Ratios (SNRs). Simulation results demonstrate that the VRKT algorithm outperforms conventional keystone transform methods in half-ambiguous velocity regions, achieving an integration gain improvement. Meanwhile, compared to classical range-spread target detection algorithms, such as energy integration, order statistics, M-out-of-N, and scattering density dependent generalized likelihood ratio test, RSTDNet demonstrates superior detection performance across various target attitudes. Furthermore, the overall VRKT-RSTDNet method is validated for high-speed range-spread target detection.