Title
Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models
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
Copyright Owner
Iowa State University
Copyright Date
2006
Language
en
File Format
application/pdf
Recommended Citation
Dogandžić, Aleksandar and Zhang, Benhong, "Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models" (2006). Electrical and Computer Engineering Publications. 8.
https://lib.dr.iastate.edu/ece_pubs/8
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.