Decentralized Bayesian algorithms for active sensor networks

The paper presents two algorithms for Decentralized Bayesian information fusion and information-theoretic decision making. The algorithms are stated in terms of operations on a general probability density function representing a single feature of the environment. Several specific density representations are then considered—Gaussian, discrete, Certainty Grid, and hybrid. Well known algorithms for these representations are shown to fit the general pattern. Stating the algorithms in Bayesian terms has a practical advantage of allowing a generic software implementation. The algorithms are described in the context of the active sensor network architecture—a modular framework for decentralized cooperative information fusion and decision making. An example of decentralized target tracking is provided. The algorithms and the framework implementation is illustrated with the results of two indoor deployment scenarios.