NDLI: Sparse estimation from sign measurements with general sensing matrix perturbation

Content Provider	IEEE Xplore Digital Library
Author	Jiang Zhu Xiaokang Lin
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Jiang Zhu; Xiaokang Lin)
Abstract	In this paper, the problem of estimating a sparse deterministic parameter vector from its sign measurements with a general perturbed sensing matrix is considered. Firstly, the best achievable mean square error (MSE) performance is explored by deriving the sparsity constrained Cramér Rao lower bound (CRLB). Secondly, the maximum likelihood (ML) estimator is utilized to estimate the unknown parameter vector. Although the ML estimation problem is non-convex, we find it can be reformulated as a convex optimization problem by re-parametrization and relaxation, which guarantees numerical algorithms to converge to the optimal point. Thirdly, a fixed point continuation (FPC) algorithm is used to solve the relaxed ML estimation problem. Finally, numerical simulations are performed to show that this relaxed method works well, and the ML estimator asymptotically approaches the CRLB as the number of measurements increases.
Starting Page	48
Ending Page	53
File Size	301842
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900711
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood estimation Sensors Vectors Digital signal processing Optimization Sparse matrices ML estimator sparse estimation sign measurements CRLB
Content Type	Text
Resource Type	Article

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