Enhanced Detection of Low-Observable Maneuvering Targets Based on FDGRFT and Lévy Golden Sparrow Search Algorithm

Low-observable maneuvering targets significantly challenge radar detection due to their low radar cross section (RCS) and high maneuverability. Within the dechirp-receiving radar system, the conventional dechirp generalized Radon–Fourier transform (DGRFT) method can measure the motion parameters of low-observable maneuvering targets through grid-based matching, which imposes a substantial computational burden. Moreover, this method performs suboptimally when the actual motion parameters deviate significantly from the discrete grid points. To overcome these limitations, this article first introduces a novel frequency-domain formulation, termed fast-frequency DGRFT (FDGRFT), which achieves computational reduction by deriving an efficient frequency-domain filter bank structure for parameter measurement. Furthermore, to address the suboptimal performance of grid-based DGRFT for off-grid parameters and the limitations of prior optimization attempts, a newly developed metaheuristic, the Lévy golden sparrow search algorithm (LGSSA), is proposed and integrated with FDGRFT. This synergistic LGSSA-FDGRFT method enables more accurate and robust measurement of target motion parameters, while further reducing computational complexity. The effectiveness of the proposed detection method for low-observable maneuvering aerial targets is validated through both simulations and measured data.