RCS Scatterer Extraction Using Apriori Target Information

摘要

Target component feature extraction is an area of considerable importance to the Ballistic Missile Defense (BMD) community. In particular, extracting essential target features from measurement data on targets of interest leads to potential target identification. The extracted component features correspond to a numerical characterization of the Geometrical Theory of Diffraction (GTD) diffraction coefficient and can also be used to develop a computationally efficient, measurement based RCS signature prediction model. A key attribute of the resulting computational model is that the measured RCS is directly incorporated into the computational model. An essential ingredient in forming a measurements-based signature model valid over a wide range of frequencies and angles is the ability to map the field measurement data (2 dimensional) onto a component-based three-dimensional (3D) geometry. To accomplish this, 3D characterization of the target scattering components is required. Typically, this 3D characterization of the scatterer locations is obtained by forming a 3D image of the target, and extracting the dominant scattering centers. In this paper we extend the novel formulation for 3D radar imaging of Inverse Synthetic Aperture Radar (ISAR) sparse- angle data using high-resolution spectral estimation theory presented in a previous paper (Ref 1) to the special case where one has apriori information about the target geometry.