Abstract
Owing to its high-fault tolerance and scalability, the consensus-based paradigm has attracted immense popularity for distributed state estimation. If a target is neither observed by a certain node nor by its neighbors, this node is naive about the target. Some existing algorithms have considered the presence of naive nodes, but it takes sufficient consensus iterations for these algorithms to achieve a satisfactory performance. In practical applications, because of constrained energy and communication resources, only a limited number of iterations are allowed and thus the performance of these algorithms will be deteriorated. By fusing the measurements as well as the prior estimates of each node and its neighbors, a local optimal estimate is obtained based on the proposed distributed local maximum a posterior (MAP) estimator. With some approximations of the cross-covariance matrices and a consensus protocol incorporated into the estimation framework, a novel distributed hybrid information weighted consensus filter (DHIWCF) is proposed. Then, theoretical analysis on the guaranteed stability of the proposed DHIWCF is performed. Finally, the effectiveness and superiority of the proposed DHIWCF is evaluated. Simulation results indicate that the proposed DHIWCF can achieve an acceptable estimation performance even with a single consensus iteration.
Highlights
Distributed state estimation has been a hot topic in the field of target tracking in sensor networks [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]
Since the prior estimates and the measurement innovation are fused with different schemes, the proposed algorithm is referred as distributed hybrid information weighted consensus filter (DHIWCF)
DHIWCF performs the best 4 compares the averaged position root mean squared error (APRMSE) of different algorithms. It shows that generalized Kalman consensus filter (GKCF) and information weighted consensus filter (ICF) obtain with limited consensus iterations L ≤ 2
Summary
Distributed state estimation has been a hot topic in the field of target tracking in sensor networks [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. Note that the redundant information only exists in the prior estimates, which come from the converged results in the previous time instant Using this property, the information weighted consensus filter (ICF) [18,20,21] divides the prior information of each node by Ns where Ns is the total number of nodes in the network. The DSIF protocol benefits from avoiding the reuse of information and offering the highest converging efficiency for network consensus, but it suffers from growing requirements of node-storage, more communication iterations, and higher communication load It takes sufficient consensus iterations for the algorithms discussed above to achieve an expected estimation performance.
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