Processing Missile-Borne SAR Data by Using Cartesian Factorized Back Projection Algorithm Integrated with Data-Driven Motion Compensation

Due to the independence of azimuth-invariant assumption of an echo signal, time-domain algorithms have significant performance advantages for missile-borne synthetic aperture radar (SAR) focusing with curve moving trajectory. The Cartesian factorized back projection (CFBP) algorithm is a newly proposed fast time-domain implementation which can avoid massive interpolations to improve the computational efficiency. However, it is difficult to combine effective and efficient data-driven motion compensation (MOCO) for achieving high focusing performance. In this paper, a new data-driven MOCO algorithm is developed under the CFBP framework to deal with the motion error problem for missile-borne SAR application. In the algorithm, spectrum compression is implemented after a CFBP process, and the SAR images are transformed into the spectrum-compressed domain. Then, the analytical image spectrum is obtained by utilizing wavenumber decomposition based on which the property of motion induced error is carefully investigated. With the analytical image spectrum, it is revealed that the echoes from different scattering points are aligned in the same spectrum range and the phase error becomes a spatial invariant component after spectrum compression. Based on the spectrum-compressed domain, an effective and efficient data-driven MOCO algorithm is accordingly developed for accurate error estimation and compensation. Both simulations of missile-borne SAR and raw data experiment from maneuvering highly-squint airborne SAR are provided and analyzed, which show high focusing performance of the proposed algorithm.