Fast Consistent Chernoff Fusion of Gaussian Mixtures for Ad Hoc Sensor Networks

This correspondence examines the Chernoff rule for robust decentralized fusion of non-Gaussian pdfs in dynamic ad hoc sensor networks. Although theoretically appealing, the Chernoff rule is challenging to implement since it leads to fusion pdfs that cannot be obtained in closed-form and requires analytically intractable optimizations. Existing heuristic approximations to the Chernoff rule are generally inconsistent and do not accurately represent the fusion pdf. A fast new procedure based on Monte Carlo importance sampling, convex optimization and weighted expectation maximization is presented here to overcome these drawbacks and enable accurate online Chernoff fusion for ad hoc distributed sensor networks with Gaussian mixtures. Numerical experiments demonstrate the superiority of the proposed procedure.

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