Efficient distributed algorithms for data fusion and node localization in mobile ad-hoc networks

Efficient distributed algorithms are an important enabling technology for large-scale ad-hoc wireless sensor and communications networks. In this paper, optimal Bayesian data fusion under the assumption of linear Gaussian state and measurement models is presented. Within this framework, an efficient algorithm for distributed state estimation in ad-hoc networks is developed. Approximate algorithms are then developed for further improvements in network resource efficiency. These include a parameterizable tradeoff of improved communications efficiency for increased latency in the rate at which information propagates through the network. It is also shown that the algorithms are well-suited for use with non-linear measurements. Finally, for distributed node position estimation in a mobile ad-hoc network, simulation results show that accurate, efficient node localization is achieved

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