PR-based SAR reconstruction algorithm for phase noise mitigation based on the hidden convexity

Abstract

The performance of synthetic aperture radar (SAR) reconstruction is significantly deteriorated by the random phase noises arising from the atmospheric turbulence or frequency jitter of the transmit signal. Recently, the emerging phase retrieval (PR) technique is gradually extended to the SAR reconstruction problem via the phase-corrupted data attributing to its alluring potential for phase noise mitigation. In this paper, a novel PR-based SAR reconstruction algorithm for phase noise mitigation is proposed by jointing alternating direction method of multipliers (ADMM) and Kolmogorov spectral factorization (KoSF). Owing to the exploiting of the hidden convexity of PR-based SAR reconstruction problem and the structure advantage of the quadratic magnitude measurement, the proposed algorithm acquires better robustness for the complex-valued Gaussian white noises and the random phase noises than the existing PR-based SAR reconstruction algorithms. In the experiments, the synthetic scene data and the moving and stationary target recognition Sandia laboratories implementation of cylinders (MSTAR SLICY) target data are provided to verify the validity of the proposed algorithm.