ON APPROXIMATE MAXIMUM LIKELIHOOD METHODS FOR BLIND IDENTIFICATION: HOW TO COPE WITH THE CURSE OF DIMENSIONALITY

被引：0

作者：

Barembruch, Steffen ^{[1
]}

Garivier, Aurelien ^{[1
]}

Moulines, Eric ^{[1
]}

机构：

[1] Inst Telecommun TELECOM ParisTech, Paris, France

来源：

2008 IEEE 9TH WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS, VOLS 1 AND 2 | 2008年

关键词：

D O I：

10.1109/SPAWC.2008.4641686

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：

We discuss approximate maximum likelihood methods for blind identification and deconvolution. These algorithms are based on particle approximation versions of the EM algorithm. We consider two different methods which differ in the way the posterior distribution of the symbols is computed. The first algorithm is based on a novel particle approximation method of the fixed-interval smoothing whereas the second uses fixed lag smoothing. We compare the two algorithms in a Monte-Carlo experiment; these two methods perform significantly better than the EMVA algorithm, which is considered as the state of the art in this area.

引用

页码：639 / 643

页数：5

共 9 条

[1] A MAXIMIZATION TECHNIQUE OCCURRING IN STATISTICAL ANALYSIS OF PROBABILISTIC FUNCTIONS OF MARKOV CHAINS [J].