水鹏朗1,
施赛楠2,
杨春娇1
1.西安电子科技大学雷达信号处理国家重点实验室 西安 10071
2.南京信息工程大学电子与信息工程学院 南京 210000
基金项目:国家自然科学基金(61871303)
详细信息
作者简介:于涵：女，1993年生，博士生，研究方向为海杂波特性分析等
水鹏朗：男，1967年生，博士，教授，研究方向为多速率滤波器理论及应用、图像处理和雷达目标检测
施赛楠：女，1990年生，博士，讲师，研究方向为雷达信号处理和微弱目标检测
杨春娇：女，1993年生，硕士，研究方向为雷达目标检测等
通讯作者:于涵 hyu_5@stu.xidian.edu.cn
中图分类号:TN958.93计量
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被引次数:0
出版历程
收稿日期:2019-03-14
修回日期:2019-08-12
网络出版日期:2019-09-03
刊出日期:2019-12-01
Combined Bipercentile Parameter Estimation of Generalized Pareto Distributed Sea Clutter Model
Han YU1,,,Penglang SHUI1,
Sainan SHI2,
Chunjiao YANG1
1. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2. School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210000, China
Funds:The National Natural Science Foundation of China (61871303)
摘要
摘要:广义Pareto分布的复合高斯模型可以很好地描述高分辨低擦地角对海探测场景中海杂波的重拖尾特性，实现该杂波模型下双参数的有效估计对雷达检测性能具有重要意义。对此，该文提出一种双参数的组合双分位点(CBiP)估计方法。该估计方法基于低阶多项式方程的显式求根表达式，充分组合利用回波中的样本信息，旨在实现高精度的双参数估计过程。此外，考虑到实际雷达工作中存在岛礁、渔船等造成的功率异常大的野点样本时，不同于传统的矩估计、最大似然(ML)估计等方法，组合双分位点估计方法仍可保持估计性能的鲁棒性。仿真及实测数据实验表明，在纯杂波环境中，组合双分位点估计方法可以实现与最大似然估计方法近似的估计精度，若存在异常样本，组合双分位点估计方法的估计性能优于上述几种传统估计方法。
关键词:参数估计/
广义Pareto分布模型/
最大似然估计/
组合双分位点估计/
野点鲁棒性
Abstract:The generalized Pareto distributed sea clutter model, known as one of the compound-Gaussian models, is able to describe heavy-tailed characteristic of sea clutter under high-resolution and low grazing angle detection scene efficiently, and the accuracy of parameter estimation under this condition heavily impacts radar’s detection property. In this paper, Combined BiPercentile (CBiP) estimator is proposed to estimate the parameters. The CBiP estimator is realized based on the explicit roots of low-order polynomial equations and full application of sample information in returns, which provides a highly-accurate parameter estimation process. Besides, the CBiP estimator can maintain the robustness of estimation performance when outliers with extremely large power are existing in samples, while other estimators, including moment-based and Maximum Likelihood (ML) estimators, degrade extremely in estimation accuracy. Without outliers in samples, the combined bipercentile estimator shows similar accuracy with the ML estimator. With outliers, the combined percentile estimator is the only method with robustness in performance, compared with other estimators aforementioned. Moreover, the ability of the new estimator is verified by measured clutter data.
Key words:Parameter estimation/
Generalized Pareto distributed clutter model/
Maximum Likelihood (ML) estimator/
Combined BiPercentile (CBiP) estimator/
Outliers-robust
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