Consensus based distributed change detection using Generalized Likelihood Ratio Methodology

In this paper a novel distributed recursive algorithm based on the Generalized Likelihood Ratio methodology is proposed for real time change detection using sensor networks. The algorithm is based on a combination of recursively generated local statistics and a global consensus strategy, and does not require any fusion center, so that the state of any node can be tested w.r.t. a given common threshold. Two different problems are discussed: detection of an unknown change in the mean and in the variance of an observed random process. Performance of the algorithm for change detection in the mean is analyzed in the sense of a measure of the error with respect to the corresponding centralized algorithm. The analysis encompasses constant and randomly time varying matrices describing communications in the network. Simulation results illustrate characteristic properties of the algorithms.

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