ANALYSIS OF FISHER INFORMATION AND THE CRAMER-RAO BOUND FOR NONLINEAR PARAMETER ESTIMATION AFTER COMPRESSED SENSING

被引：0

作者：

Pakrooh, Pooria ^{[1
]}

Scharf, Louis L. ^{[2
]}

Pezeshki, Ali ^{[1
]}

Chi, Yuejie ^{[3
]}

机构：

[1] Colorado State Univ, ECE Dept, Ft Collins, CO 80523 USA

[2] Colorado State Univ, Dept Math & Stat, Ft Collins, CO 80523 USA

[3] Ohio State Univ, Dept ECE & Biomed Informat, Columbus, OH 43210 USA

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2013年

关键词：

Cramer-Rao bound; compressed sensing; Fisher information; Johnson-Lindenstrauss Lemma; parameter estimation; MATRICES; PROOF;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this paper, we analyze the impact of compressed sensing with random matrices on Fisher information and the CRB for estimating unknown parameters in the mean value function of a multivariate normal distribution. We consider the class of random compression matrices that satisfy a version of the Johnson-Lindenstrauss lemma, and we derive analytical lower and upper bounds on the CRB for estimating parameters from randomly compressed data. These bounds quantify the potential loss in CRB as a function of Fisher information of the non-compressed data. In our numerical examples, we consider a direction of arrival estimation problem and compare the actual loss in CRB with our bounds.

引用

页码：6630 / 6634

页数：5

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