Distributed fusion estimation with square-root array implementation for Markovian jump linear systems with random parameter matrices and cross-correlated noises

The distributed fusion estimation is presented for Markovian jump linear systems with random parameter matrices and cross-correlated noises in sensor networks.The LMMSE estimator is derived based on Gram-Schmidt orthogonal innovation sequence under a centralized framework.The square-root array implementation is further presented by recursively triangularizing the square roots of related positive semidefinite matrices in the LMMSE estimator.Via the information filter form, the distributed fusion estimation with square-root array implementation is proposed, incorporated with consensus strategy. The convergent condition and the convergence point are also discussed with respect to consensus strategy. This study presents the distributed fusion estimation of discrete-time Markovian jump linear systems with random parameter matrices and cross-correlated noises in sensor networks. The recursive linear minimum mean square error estimator is proposed based on the Gram-Schmidt orthogonalization procedure under a centralized framework. In order to avoid the loss of positive semidefiniteness and reduce dynamical range, its square-root array implementation is presented by recursively triangularizing the square roots of relevant positive semidefinite matrices. Furthermore, via the information filter form, the distributed fusion estimation with square-root array implementation is derived from the centralized fusion structure, incorporated with consensus strategy. A maneuvering target tracking simulation in a sensor network validates the proposed method.

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