IGAMF: Adaptive CFAR Detection and Blind Speed Sidelobe Suppression for High-Speed Target in Homogeneous Environment

Abstract

In this article, high-speed target detection in homogeneous clutter is investigated, where both blind speed sidelobe (BSSL) suppression and constant false alarm rate (CFAR) property are considered. The Radon-Fourier transform (RFT) and adaptive Radon-Fourier transform (ARFT) have been proposed for high-speed target detection in noise and clutter background, respectively, which can perform long-time coherent integration (LTCI) for high-speed target and therefore have excellent detectability potential. However, they do not possess the CFAR property and suffer from the BSSL problem, leading to the degradation of detection performance. To address these two problems, the iterative generalized adaptive matched filter (IGAMF) is proposed in this article, which achieves target CFAR detection and BSSL suppression by detecting the targets one by one and eliminating the interference from the detected targets. The simulation results demonstrate that the IGAMF has a higher detection probability compared to RFT and ARFT, and can effectively address the BSSL problem.