利用近似展开法的多视K模型参数估计器性能分析

准确估计模型参数是对合成孔径雷达图像空变背景杂波精确建模的重要前提。K分布是一种广泛应用于描述合成孔径雷达（Synthetic Aperture Radar，SAR）图像杂波统计特性的概率模型，针对多视K分布杂波模型，该文总结了现有的参数估计方法，将常见的9种矩估计器推广到多视SAR图像的杂波建模中，并采用近似展开法推导了各估计器估计偏差和方差的解析表达式。本文对各种估计器的性能进行了对比与分析，结果表明，估计器的性能取决于视数、样本数、待估计阶参数的取值范围以及估计器的计算量，并且得到了不同应用场景与条件下合理选择估计器的一般原则。在实际应用中，应该根据具体情况来选择合适的估计器。 

Performance Analysis of Multilook K-Model Parameter Estimators Using the Approach of Approximate Expansion

Parameter estimation is a crucial task for accurately model the synthetic aperture radar (SAR) clutter with various terrain classes. K-distribution has been proved to be a good model for describing the statistical properties of clutter in multi-look synthetic aperture radar (SAR) images. Methods of estimating the parameters of multilook K-distribution are briefly reviewed and nine different moment-based estimators are extended to modeling the multilook SAR imagery clutter. Moreover, analytic s of the biases and variances of estimators are derived through the approach of approximate expansion. Based on the performance comparison and analysis, some basic guide lines for estimator selection under different circumstances are also given, and the results show that the performance of the estimator depends on the number, number of samples, the scope of order parameter and the estimator of the amount of calculation. In practice, we should on the basis of the specific situation to choose suitable estimator.