一种面向机械旋转变极化雷达的蝙蝠超分辨改进算法OA

In recent years,bionic super-resolution technology,inspired by biological perception mechanisms,has emerged as a substantial research direction aimed at overcoming the limitations of radar resolution.The Baseband Spectrogram Correlation and Transformation(BSCT)model,which is based on bat hearing,offers a novel approach to enhancing traditional radar resolution.However,the model exhibits inherent limitations,including insufficient multi-target adaptability and the inability to utilize polarization information.To address these problems,this paper proposes a polarization-enhanced bionic super-resolution model:Polarimetric Baseband Spectrogram Correlation and Transformation(P-BSCT)for Mechanical Rotation Polarimetric Radar(MRPR).The primary contributions of this study are as follows:first,the integration of the bat BSCT model with MRPR,thereby enabling the utilization of polarization information and the execution of polarization measurements;second,the proposal of an advanced signal processing method,which overcomes the limitations of the original BSCT in two-target and static scenes,effectively applying to multi-target and moving-target scenarios,and exhibiting no impact on the resolution effect due to signal modulation.P-BSCT has been demonstrated to enhance resolving power by approximately 15 dB under optimal conditions when compared with the original BSCT model.In scenarios involving moving targets,targets exhibiting equivalent polarization scattering properties,and nonlinear FM signals,the resolving performance of P-BSCT remains essentially unchanged,demonstrating notable robustness.