逆伽马纹理复合高斯杂波参数的贝叶斯估计方法  

Bayesian estimation method for Compound Gaussian Clutter model with inverse Gamma texture

作　　者：许述文[1] 王乐 曾威良 水鹏朗[1] XU Shuwen;WANG Le;ZENG Weiliang;SHUI Penglang(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an Shaanxi 710071,China)

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室

出　　处：《太赫兹科学与电子信息学报》2019年第4期583-588,共6页Journal of Terahertz Science and Electronic Information Technology

基　　金：陕西省自然科学基金面上项目资助项目(2017JM6031);陕西省科协人才托举计划资助项目(20160205)

摘　　要：复合高斯杂波(CGC)在拟合高分辨力、低掠射角海杂波中已得到广泛应用,带有逆伽马纹理的CGC的强度分布为广义帕累托(GP)分布。在实际雷达工作场景中,由于观测区域内海杂波的非平稳非均匀特性,导致独立同分布杂波样本的获取十分困难。提出一种广义帕累托分布参数的贝叶斯估计方法,通过在线更新数据的先验信息,获取小样本情形下GP分布参数。仿真实验证明,该方法能够在样本数量较小的情况下,对GP数据实现较为精确的参数估计。Compound Gaussian Clutter(CGC)model has been widely used in fitting sea clutter with high resolution and low grazing angle.The intensity distribution of CGC with inverse Gamma texture is Generalized Pareto(GP)distribution.In real radar environment,it is very difficult to obtain large-size independent and identically distributed clutter samples due to the non-stationary and non-uniform characteristics of sea surface.Therefore,a Bayesian estimation method for parameters of GP distribution is proposed.The parameters of GP distribution with small sample are obtained by updating the prior knowledge of data online.Simulation results show that the proposed method can achieve more accurate parameter estimation compared with the conventional methods when the prior knowledge is reliable.

关 键 词：海杂波 帕累托分布 参数估计 贝叶斯 

分 类 号：TN914.42[电子电信—通信与信息系统]