Coherent Integration for Maneuvering Target Detection at Low SNR Based on Radon-General Linear Chirplet Transform
Min Bao, Boyang Jia, Yachao Li, Liang Guo, Mengdao Xing
Abstract
This letter considers the coherent integration problem for a maneuvering target in low signal-to-noise-ratio (SNR) circumstances. Focusing on the range migration (RM) and Doppler frequency migration (DFM) problems caused by the motion of the target, we propose a new method called Radon-general linear chirplet transform (RGLCT). Jointly motion parameters search is employed to obtain the trajectory of the maneuvering target and the coherent integration is achieved via general linear chirplet transform (GLCT). Because of the non-sensitive-to-noise feature of the GLCT, RGLCT can realize weak target coherent integration in very low SNR environments. Multi-target detection can be achieved successfully because the GLCT is not influenced by the cross-term components. Finally, simulations and real data experiments are performed to demonstrate the effectiveness of the method. The results show that the proposed method has superior detection ability than methods including Radon-Fourier transform (RFT), and Radon-Lv’s distribution (RLVD). Both theory and experiments have fully proved that the proposed method can effectively realize coherent integration in low SNR environments.