Super-resolution feature extraction imaging algorithm for complex objects

For the goal of an Object Recognition (OR) Radar system, a feature extraction algorithm is proposed in this paper. Conventional radar imaging method based on Kirchhoff Migration and the revised range point migration method are known to obtain fast and accurate images. However, these methods are not suitable for feature extraction as the Kirchhoff Migration method processes the whole evolution of the Radar data and the revised range point migration method extracts the coordinates of the target contour not features. Furthermore, the new proposed feature extraction algorithm is designed for a circular scanning trajectory, or a rotating target and a bi-static antenna configuration. The proposed algorithm calculates the target points from the a priori extracted wavefronts of the Object Under Test (OUT). A Polarimetric Dynamic Correlation Method (PDCM) is employed in the proposed algorithm for the extraction of the wavefronts. Experimental validations are performed with two complex OUT, a M-sequence Radar device (4.5 GHz-13.5 GHz) and compact dual-polarized Ultra-Wideband antennas.