RCS Feature Extraction Using Discretized Point Scatterer With Compressive Sensing

摘要

This letter presents a novel technique for radar cross section (RCS) feature extraction using discrete scattering center modeling and a basis pursuit denoising (BPDN) algorithm for compressive sensing. From the Stratton-Chu formula, a high-frequency assumption has been applied to define the target object as a combination of independent point scatterers. Using the BPDN solver, complex-valued scattering sources are determined from a matrix equation for the scattering problem. Using a numerical example, it has been verified that the proposed method can extract the RCS feature accurately, and the measurement efficiency is much higher compared to that of conventional methods.