On MMSE Estimation: A Linear Model Under Gaussian Mixture Statistics

被引：40

作者：

Flam, John T. ^{[1
]}

Chatterjee, Saikat ^{[2
]}

Kansanen, Kimmo ^{[1
]}

Ekman, Torbjorn ^{[1
]}

机构：

[1] NTNU Norwegian Univ Sci & Technol, Dept Elect & Telecommun, NO-7034 Trondheim, Norway

[2] KTH Royal Inst Technol, Sch Elect Engn, Commun Theory Lab, S-10044 Stockholm, Sweden

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2012年 / 60卷 / 07期

关键词：

Gaussian mixture distribution; linear model; minimum mean square error estimation; BAYESIAN-ESTIMATION; ESTIMATION ERROR; SPEECH;

D O I：

10.1109/TSP.2012.2192112

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In a Bayesian linear model, suppose observation y = Hx + n stems from independent inputs x and n which are Gaussian mixture (GM) distributed. With known matrix H, the minimum mean square error (MMSE) estimator for x, has analytical form. However, its performance measure, the MMSE itself, has no such closed form. Because existing Bayesian MMSE bounds prove to have limited practical value under these settings, we instead seek analytical bounds for the MMSE, both upper and lower. This paper provides such bounds, and relates them to the signal-to-noise-ratio (SNR).

引用

页码：3840 / 3845

页数：6

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