High-frequency Phase Error Reduction in Sar Using Particle Swarm of Optimization Algorithm

Abstract

Since 1970, various autofocus algorithms have been developed to improve synthetic aperture radar (SAR) image quantity by removing its residual phase errors after conventional motion compensation. In particular, the high-frequency SAR phase error is formulated as a nonlinear constrained optimization problem in and a genetic algorithm based autofocus technique is used to minimize the phase noise at the expense of high computational cost. This paper presents a relatively simple and computational efficient approach to solve the high-frequency SAR phase noise. The proposed algorithm utilizes a particle swarm optimization (PSO) technique, which has been successfully applied in many applications due to its robustness and simplicity. The power-to-spreading noise ratio (PSR) is used as the focal quality indicator to search for optimum solution. The algorithm is tested on simulated targets and the results show significant improvement in the phase measurements of SAR signals. Furthermore, the proposed approach outperforms the former genetic algorithm based technique in terms of the computational time and solution errors — which clearly demonstrates the potential of this approach in SAR autofocusing.