A Novel Method for Abrupt Motion Change Radar Target Detection Based on Generalized Radon-Fourier Transform

Long-time integration is an effective method to improve the signal-to-noise ratio (SNR) and detection performance of highly maneuvering targets. The existing methods for long-time integration mainly focus on detecting targets with high-order motion under fixed model, losing sight of the possibility that targets may have an abrupt motion change at an unknown instant. Therefore, this paper proposes a novel long-time integration method for highly maneuvering target with abrupt change model (ACM) based on generalized Radon-Fourier transform (GRFT), which could coherently integrate the target echoes before and after ACM and obtain the optimal coherent integration gain. The simulation and experiment results show that the proposed method can detect the ACM target under the condition of extremely low SNR and improve the detection performance compared with the fixed model GRFT.