NDLI: Maximum Likelihood Estimation From Sign Measurements With Sensing Matrix Perturbation

Content Provider	IEEE Xplore Digital Library
Author	Jiang Zhu Xiaohan Wang Xiaokang Lin Yuantao Gu
Copyright Year	1991
Abstract	The problem of estimating an unknown deterministic parameter vector from sign measurements with a perturbed sensing matrix is studied in this paper. We analyze the best achievable mean-square error (MSE) performance by exploring the corresponding Cramér-Rao lower bound (CRLB). To estimate the parameter, the maximum likelihood (ML) estimator is utilized and its consistency is proved. We show that, compared with the perturbed-free setting, the perturbation on the sensing matrix exacerbates the performance of the ML estimator in most cases. However, suitable perturbation may improve the performance in some special cases. Then, we reformulate the original ML estimation problem as a convex optimization problem, which can be solved efficiently. Furthermore, theoretical analysis implies that the perturbation-ignored estimation is a scaled version with the same direction of the ML estimation. Finally, numerical simulations are performed to validate our theoretical analysis.
Sponsorship	IEEE Signal Processing Society
Starting Page	3741
Ending Page	3753
Page Count	13
File Size	2911597
File Format	PDF
ISSN	1053587X
Volume Number	62
Issue Number	15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Sensors Vectors Noise Noise measurement Convex functions sign measurements CRLB Gaussian perturbation maximum likelihood estimation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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