Pr-Based Sar Reconstruction Autofocus Algorithm for Persistent Surveillance Change Detection

Abstract

Random phase noises arising from frequency jitter of transmit signal and atmospheric turbulence result in corrupted synthetic aperture radar (SAR) imagery, which in turn degrades change detection (CD) performance. In this paper, a phase retrieval (PR) based SAR reconstruction autofocus framework by exploiting the hidden convexity is proposed with the goal of achieving reliable persistent surveillance CD. Firstly the original non-convex quartic SAR reconstruction is reformulated as a convex quadratic program. Under the minimum phase assumption, the auto-correlation retrieval- Kolmogorov factorization (CoRK) algorithm is then utilized to optimally and efficiently retrieve the underlying SAR reflectivity. The devised scheme possesses effective capabilities of phase noise mitigation, thus has a superior CD performance. Experimental results are provided to verify the effectiveness of the proposed method.