A Diffusion-Based EM Algorithm for Distributed Estimation in Unreliable Sensor Networks

We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an e...

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Vydané v:IEEE signal processing letters Ročník 20; číslo 6; s. 595 - 598
Hlavní autori: Pereira, S. S., Lopez-Valcarce, R., Pages-Zamora, A.
Médium: Journal Article Publikácia
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2013
Predmet:
Consensus averaging
Diffusion strategies
Distributed estimation
Enginyeria electrònica
Expectation-maximization
Instrumentació i mesura
Maximum likelihood estimation
Maximum-likelihood
Noise measurement
Sensor networks
Sensors i actuadors
Signal processing algorithms
Signal to noise ratio
Soft detection
Wireless sensor networks
Xarxes de sensors
Àrees temàtiques de la UPC
ISSN:1070-9908, 1558-2361
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Shrnutí:We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer-Rao Lower Bound at all SNR values and compares favorably with other approaches.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2013.2260329