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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.
CitationSilva, S., López, R., Pages, A. A diffusion-based em algorithm for distributed estimation in unreliable sensor networks. "IEEE signal processing letters", Gener 2013, vol. 20, núm. 6, p. 595-598.
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