Identification of sparse FIR systems using a general quantisation scheme

被引：27

作者：

Godoy, Boris I. ^{[1
]}

Agueero, Juan C. ^{[1
]}

Carvajal, Rodrigo ^{[2
]}

Goodwin, Graham C. ^{[1
]}

Yuz, Juan I. ^{[2
]}

机构：

[1] Univ Newcastle, Ctr Complex Dynam Syst & Control, Callaghan, NSW 2308, Australia

[2] Univ Tecn Federico Santa Maria, Dept Elect, Valparaiso, Chile

来源：

INTERNATIONAL JOURNAL OF CONTROL | 2014年 / 87卷 / 04期

关键词：

system identification; quantised systems; sparsity; maximum likelihood; VARIANCE-MEAN MIXTURES; CARLO EM ALGORITHM; OUTPUT OBSERVATIONS; VARIABLE SELECTION; CHANNEL ESTIMATION; REGRESSION-MODELS; DATA AUGMENTATION; DISTRIBUTIONS; REPRESENTATIONS; IMAGE;

D O I：

10.1080/00207179.2013.861611

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an identification scheme for sparse FIR systems with quantised data. We consider a general quantisation scheme, which includes the commonly deployed static quantiser as a special case. To tackle the sparsity issue, we utilise a Bayesian approach, where an l(1) a priori distribution for the parameters is used as a mechanism to promote sparsity. The general framework used to solve the problem is maximum likelihood (ML). The ML problem is solved by using a generalised expectation maximisation algorithm.

引用

页码：874 / 886

页数：13

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