存在未知额外参数的散点矩阵估计:不匹配情况

2016 24th European Signal Processing Conference (EUSIPCO) Pub Date : 2016-08-01 DOI:10.1109/EUSIPCO.2016.7760635

S. Fortunati, F. Gini, M. Greco

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引用次数: 4

摘要

本文给出了在错误数据模型下散射矩阵与复椭圆对称分布向量幂的联合估计的约束错匹配极大似然估计。具体来说，该估计量是通过假设正态模型而从复t分布中采样数据来获得的。研究了该CMML估计量的收敛点，并将其均方误差(MSE)与约束错定cram - rao界(CMCRB)进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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On scatter matrix estimation in the presence of unknown extra parameters: Mismatched scenario

In this paper, a Constrained Mismatched Maximum Likelihood (CMML) estimator for the joint estimation of the scatter matrix and the power of Complex Elliptically Symmetric (CES) distributed vectors is derived under misspecified data models. Specifically, this estimator is obtained by assuming a Normal model while the data are sampled from a complex t-distribution. The convergence point of such CMML estimator is investigated and its Mean Square Error (MSE) compared with the Constrained Misspecified Cramér-Rao Bound (CMCRB).

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2016 24th European Signal Processing Conference (EUSIPCO)

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