NDLI: Distributed estimation in sensor networks with imperfect model information: An adaptive learning-based approach

Content Provider	IEEE Xplore Digital Library
Author	Qing Zhou Kar, S. Huie, L. Shuguang Cui
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA (Kar, S.) \|\| Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, USA (Qing Zhou; Shuguang Cui) \|\| Air Force Research Lab, 525 Brooks Road, Rome, NY 13441, USA (Huie, L.)
Abstract	The paper considers the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in wireless sensor networks (WSNs), in which each sensor receives a single snapshot of the field. The observation or sensing mode is only partially known at the corresponding nodes, perhaps, due to their limited sensing capabilities or other unpredictable physical factors. Specifically, it is assumed that the observation process at a node switches stochastically between two modes, with mode one corresponding to the desired signal plus noise observation mode (a valid observation), and mode two corresponding to pure noise with no signal information (an invalid observation). With no prior information on the local sensing modes (valid or invalid), the paper introduces a learning-based distributed estimation procedure, the mixed detection-estimation (MDE) algorithm, based on closed-loop interactions between the iterative distributed mode learning and estimation. The online learning (or sensing mode detection) step re-assesses the validity of the local observations at each iteration, thus refining the ongoing estimation update process. The convergence of the MDE algorithm is established analytically. Simulation studies show that, in the high signal-to-noise ratio (SNR) regime, the MDE estimation error converges to that of an ideal (centralized) estimator with perfect information about the node sensing modes. This is in contrast with the estimation performance of a naive average consensus based distributed estimator (with no mode learning), whose estimation error blows up with an increasing SNR.
Starting Page	3109
Ending Page	3112
File Size	135183
Page Count	4
File Format	PDF
ISBN	9781467300452
ISSN	15206149
e-ISBN	9781467300469
e-ISBN	9781467300445
DOI	10.1109/ICASSP.2012.6288573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-25
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Signal to noise ratio Limiting Convergence Estimation error Sensors sensor networks Distributed estimation distributed learning adaptive stochastic switching
Content Type	Text
Resource Type	Article

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