Campus Units

Electrical and Computer Engineering

Document Type

Article

Publication Date

8-2006

Journal or Book Title

IEEE Transactions on Signal Processing

Volume

54

Issue

8

First Page

3200

Last Page

3215

DOI

10.1109/TSP.2006.877659

Abstract

We develop a hidden Markov random field (HMRF) framework for distributed signal processing in sensor-network environments. Under this framework, spatially distributed observations collected at the sensors form a noisy realization of an underlying random field that has a simple structure with Markovian dependence. We derive iterated conditional modes (ICM) algorithms for distributed estimation of the hidden random field from the noisy measurements. We consider both parametric and nonparametric measurement-error models. The proposed distributed estimators are computationally simple, applicable to a wide range of sensing environments, and localized, implying that the nodes communicate only with their neighbors to obtain the desired results. We also develop a calibration method for estimating Markov random field model parameters from training data and discuss initialization of the ICM algorithms. The HMRF framework and ICM algorithms are applied to event-region detection. Numerical simulations demonstrate the performance of the proposed approach

Comments

This is a post-print of an article from IEEE Transactions on Signal Processing 54 (2006): 3200–3215, doi:10.1109/TSP.2006.877659. Posted with permission.

Copyright Owner

Iowa State University

Language

en

File Format

application/pdf

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