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
收藏
页码：6630 / 6634
页数：5
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