Performance characterization of Phase Gradient Autofocus for inverse synthetic aperture LADAR

Phase Gradient Autofocus (PGA) is an effective algorithm for estimating and removing piston-phase errors from spotlight-mode synthetic aperture radar (SAR) data. For target scenes dominated by a point source, the algorithm has been shown to be optimal in the sense that it approaches the Cramér-Rao bound for carrier-to-noise ratios (CNRs) as low as -5 dB. In this paper, we explore PGA's effectiveness against ground-based inverse synthetic aperture LADAR (ISAL) observations of spacecraft, where the target characteristics and phase errors are quite different than in the SAR case. At optical wavelengths, the power spectrum of the piston-phase errors will be dominated less by platform motion and more by atmospheric variations. In addition, space objects will have fewer range-resolution cells across them than would a typical extended SAR scene. This research characterizes the performance limitations of PGA for an ISAL system as a function of CNR and the number of range-resolution cells across the scene. A high-fidelity wave-optics simulation is used to generate representative test data for input to the PGA algorithm. Emphasis is placed on finding the lower limits of performance for which image reconstruction is possible.