对Jahangir组合式矩估计器的改进

李大朋; 姚迪

对Jahangir组合式矩估计器的改进

李大朋,
姚迪

北京理工大学, 电子信息工程学院, 北京 100081

详细信息

中图分类号: TN911.23
计量
- 文章访问数: 3135
- HTML全文浏览量: 199
- PDF下载量: 515
- 被引次数: 0
出版历程
- 收稿日期: 2011-03-11
- 网络出版日期: 2012-06-30

Improvement of Jahangir’s multiple moments estimator

Li Dapeng,
Yao Di

School of Information and Electronics, Beijing Institute of Technology, Beijing 10081, China

摘要

摘要: 在M.Jahangir以常数为权的组合式矩估计器的基础上,给出一种以函数为权的组合式矩估计器,称为L-J估计器.其中,最优加权函数是根据U估计器与形状参数的单调关系,通过数论网格最优化算法搜索解出.大量仿真实验证实,在对K分布形状参数v大范围的参数估计中,L-J估计器在估计精度上,不但较Jahangir等提出的常数加权组合矩估计器的精度有显著提高,而且可与MLE(Maximum Likelihood Estimator)相当.特别是由于MLE作为渐进无偏估计量,需要充分大的样本长度才能达到最优,这就使得L-J估计器的估计精度可在样本长度较小时优于MLE.此外,L-J估计器无需迭代运算,因而在计算效率上,显著优于现有的ML估计器.
- K分布 /
- U估计器 /
- 组合式矩估计器 /
- ML估计器
Abstract: Based on M.Jahangir-s multiple moments estimator using constant weight, a new estimator named L-J estimator was proposed, which consists of multiple moments and uses function weight. The optimum weight function was obtained by the sequential algorithm for optimization according to the monotonic relationship of U-estimator and the shape parameter. A large number of simulation experiments show that the accuracy of L-J estimator is not only higher than that of Jahangir-s multiple estimator using constant weight noticeably, but also it can stand comparison with that of maximum likelihood estimator (MLE). As an asymptotic unbiased estimation, MLE requires sufficient large number of samples to achieve the optimum performance, then it makes that the accuracy of L-J estimator can be better than that of MLE in the case of fewer samples. Moreover, the efficiency of L-J estimator is obviously higher than that of MLE, since there is no iteration to need.
- K-distribution /
- U-estimator /
- multiple moments estimator /
- maximum likelihood estimator

HTML全文

参考文献(12)

[1]	Armstrong B C,Griffiths H D.CFAR detection of fluctuating targets in spatially correlated K-distributed clutter[J].IEE Proceedings-F,1991,138(2):139-152
[2]	Joughin I R,Percival D B,Winebrenner D P.Maximum likelihood estimation of K-distribution parameters for SAR data[J].IEEE Trans on Geosciences and Remote Sensing,1993,31(5):989-999
[3]	Blacknell D.Comparison of parameter estimators for K-distribution[J].IEE Proc-Radar,Sonar,Navigation,1994,141(1): 45-52
[4]	Jahangir M,Blacknell D,White R G.Accurate approximation to the optimum parameter estimate for K-distributed clutter[J].IEE Proc-F,1996,143(6):383-390
[5]	郝程鹏,侯朝焕,鄢锦.一种新的K分布形状参数估计器[J].电子与信息学报,2005,27(9):1404-1407 Hao Chengpeng,Hou Chaohuan,Yan Jin.A new estimator for estimating the parameters of K-distribution[J].Journal of Electronics & Information Technology,2005,27(9):1404-1407(in Chinese)
[6]	郝程鹏,侯朝焕.一种K分布杂波背景下的双参数恒虚警检测器[J].电子与信息学报,2007,29(3):756-759 Hao Chengpeng,Hou Chaohuan.A two parameter CFAR detector in K-distribution cluuter[J].Journal of Electronics & Information Technology,2007,29(3):756-759(in Chinese)
[7]	张德丰.MATLAB数值计算方法[M].北京:机械工业出版社,2010:143-147 Zhang Defeng.Numerical computing with MATLAB[M].Beijing:Machinery Industry Press,2010:143-147(in Chinese)
[8]	William J J Roberts,Sadaoki Furui.Maximum likehood estimation of K-distribution via the expectation-maximization algorithm[J].IEEE Transaction on Signal Processing,2000,48(12):3303-3306
[9]	胡文琳,王永良,王首勇.基于zlog(z)期望的K分布参数估计[J].电子与信息学报,2008,30(1):203-205 Hu Wenlin,Wang Yongliang,Wang Shouyong.Estimation of the parameters of K-distribution based on zlog(z) expectation[J].Journal of Electronics & Information Technology,2008,30(1):203-205(in Chinese)
[10]	Abraham D A,Lyons A P.Reliable methods for estimating the K-distribution shape parameter[J].Oceanic Engineering,IEEE Journal of,2010,35(2):288-302
[11]	娄昊,张乐锋.雷达海杂波的K分布复合特性[J].火力与指挥控制,2010,35(7):62-65 Lou Hao,Zhang Lefeng.K-distribution compound characteristics research on radar sea clutter[J].Fire Contral and Command Contral,2010,35(7):62-65(in Chinese)
[12]	张晓利,关键,何友.K分布杂波中分布式目标的Rao检测[J].电子与信息学报,2010,32(10):2496-2410 Zhang Xiaoli,Guan Jian,He You.Rao test of distributed targets in K-distributed clutter[J].Journal of Electronics & Information Technology,2010,32(10):2496-2410(in Chinese)