This paper is concerned with the problem of distributed estimation fusion over peer-to-peer asynchronous sensor networks with random packet dropouts. A distributed asynchronous fusion algorithm is proposed via the covariance intersection method. First, local estimator is developed in an optimal batch fashion by constructing augmented measurement equations. Then the fusion estimator is designed to fuse local estimates in the neighborhood. Both local estimator and fusion estimator are developed by taking into account the random packet losses. The presented estimation method improves local estimates and reduces the estimate disagreement. Simulation results validate the effectiveness of the proposed distributed asynchronous fusion algorithm.
A distributed sequential fusion algorithm for decentralized asynchronous sensor networks is presented in this paper. Our distributed asynchronous sequential fusion algorithm can be divided into a local estimator and a fusion estimator. The local estimator utilize the local measurements to generate a local fusion estimates. And the local fusion estimates are fused by the fusion estimator to improve the local estimation performance. The proposed algorithm is operational for sensor networks with arbitrarily sampling rates and initial sampling time instants. The effectiveness of the proposed algorithm is validated by simulation results.
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