基于粒子群优化的极化SAR定标算法

Polarimetric SAR Calibration Based on Particle Swarm Optimization

极化合成孔径雷达(SAR)的精确定标对于极化数据的终端应用至关重要。提出了一种基于粒子群优化的极化SAR定标算法,由于在估计误差参数过程中没有作任何的近似,相比于经典的极化定标算法,该方法即使在串扰参数较高时仍能保持很高的精确度。为了验证算法的有效性,分别使用仿真数据和真实的机载极化SAR数据来模拟极化失真数据,并采用多种极化定标方法对失真数据进行校准,校准结果证明了所提方法对极化误差参数有着更加稳定精确的估计。

The accurate calibration of polarimetric SAR(PolSAR)is critical to end applications of polarimetric data.In this paper,a PolSAR calibration algorithm based on particle swarm optimization is proposed.Since there is no approximation in the process of the parameter estimation,this algorithm can accurately solve all parameters compared to classical algorithms,even if the crosstalk is high.To verify the effect of the proposed calibration algorithm,we simulate distorted data using the simulated data and real airborne PolSAR data,and calibrate the distorted data with various algorithms.The calibration results confirm that the proposed algorithm can solve all calibration parameters more stably and accurately.