Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104813
Title: Distributed local linear parameter estimation using gaussian SPAWN
Authors: Leng, Mei
Tay, Wee Peng
Quek, Tony Q. S.
Shin, Hyundong
Keywords: Diffusion
DRNTU::Engineering::Electrical and electronic engineering
Belief Propagation
Issue Date: 2015
Source: Leng, M., Tay, W. P., Quek, T. Q. S., & Shin, H. (2015). Distributed Local Linear Parameter Estimation Using Gaussian SPAWN. IEEE Transactions on Signal Processing, 63(1), 244-257. doi:10.1109/TSP.2014.2373311
Series/Report no.: IEEE Transactions on Signal Processing
Abstract: We consider the problem of estimating local sensor parameters, where the local parameters and sensor observations are related through linear stochastic models. We study the Gaussian Sum-Product Algorithm over a Wireless Network (gSPAWN) procedure. Compared with the popular diffusion strategies for performing network parameter estimation, whose communication cost at each sensor increases with increasing network density, gSPAWN allows sensors to broadcast a message whose size does not depend on the network size or density, making it more suitable for applications in wireless sensor networks. We show that gSPAWN converges in mean and has mean-square stability under some technical sufficient conditions, and we describe an application of gSPAWN to a network localization problem in non-line-of-sight environments. Numerical results suggest that gSPAWN converges much faster in general than the diffusion method, and has lower communication costs per sensor, with comparable root-mean-square errors.
URI: https://hdl.handle.net/10356/104813
http://hdl.handle.net/10220/47836
ISSN: 1053-587X
DOI: 10.1109/TSP.2014.2373311
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TSP.2014.2373311
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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