基于模型相似度拟合的海杂波统计方法

摘要

本文提出一种基于模型相似度拟合的海杂波统计方法。首先根据合成孔径雷达（SAR）图像计算瑞利分布、对数正态分布、韦布尔分布、K 分布、G0分布5种经典的海杂波分布的概率密度函数，然后根据模型间的相似度准则拟合得到新的海杂波分布模型。文章利用四景不同类型的真实 SAR 数据对算法的拟合性能进行了评价，结果显示利用该算法得到的拟合模型与真实 SAR 数据的平均Kullback-Leibler 距离仅为0．01584，远优于其他分布模型。基于该拟合模型的恒虚警率舰船检测算法对四景 SAR 数据的平均检测精度达到95．75％，在控制虚警和漏检方面均优于采用其他模型的同类方法。%A sea clutter statistical method based on similarity fitting is proposed in this paper.We first estimate five classical probability density functions of the sea clutter distribution in synthetic aperture radar (SAR)imagery, which include Rayleigh distribution,lognormal distribution,Weibull distribution,K distribution and G0 distribution. Thereafter,we fit all these five models to a new sea clutter distribution by an optimization method based on a simi-larity criterion.In the experiment,we evaluate the fitting precision based on the Kullback-Leibler distance using four SAR images.The results show the K-L distance between the fitting model and observations is only 0.015 84 when using the proposed algorithm.The fitting model is concluded to be superior to five classical distribution mod-els.When we conduct ship detection using the fitting model and CFAR algorithm,the mean detection accuracy can reach up to 95.75%.