HRRP Target Recognition Method Assisted by Scattering Center Prior Fusion from SAR Images

Accurate target prior information is an important basis for improving the detection and recognition performance of high-resolution range profiles (HRRPs). To address the insufficient reliability of prior templates in scattering-center-assisted HRRP target recognition based on SAR images, where scattering centers extracted from a single image are susceptible to noise, sidelobes, local scattering fluctuations, and variations in observation azimuth angle, this paper proposes an HRRP target recognition method based on the fusion of scattering-center priors derived from SAR images acquired at adjacent azimuth angles. Two-dimensional scattering centers at different azimuth angles are extracted by combining an adaptive point-spread function (PSF) with the two-dimensional CLEAN algorithm. Amplitude-weighted clustering is then employed to retain stable scattering structures with spatial consistency, thereby constructing a fused scattering center model. Subsequently, the fused model is projected onto the range dimension under different azimuth offsets to establish a one-dimensional scattering center prior template library. Target classification is finally performed using sliding matching based on the maximum correlation coefficient. Same-band, cross-band, and comparative experiments conducted with ground-target data sets demonstrate that the proposed method effectively improves the stability of the prior templates and target recognition performance, while exhibiting reasonable adaptability to variations in observation azimuth angle, noise level, and radar operating frequency band.