Abstract
Consensus algorithm for networked dynamic systems is an important research problem for data fusion in sensor networks. In this paper, the distributed filter with consensus strategies known as Kalman consensus filter and information consensus filter is investigated for state estimation of distributed sensor networks. Firstly, an in-depth comparison analysis between Kalman consensus filter and information consensus filter is given, and the result shows that the information consensus filter performs better than the Kalman consensus filter. Secondly, a novel optimization process to update the consensus weights is proposed based on the information consensus filter. Finally, some numerical simulations are given, and the experiment results show that the proposed method achieves better performance than the existing consensus filter strategies.
Highlights
In recent years, there has been a surge of interests in the area of distributed sensor networks
Based on the information consensus filter (ICF), we focus on designing the consensus weights to improve its performance
Based on the ICF, we propose the consensus weights optimization for better performance of the system and refer this method as weights optimized information consensus filter (WO-ICF)
Summary
There has been a surge of interests in the area of distributed sensor networks. One of the most basic problems for distributed sensor networks is to develop distributed algorithms [1] for the state estimation of a process of interest. Olfati-Saber developed the Kalman consensus filter (KCF) in [11], where a consensus filter runs directly on the estimator state space variables. An information consensus filter (ICF) is presented in [16] that applies consensus filters to an information filter This method does not exactly solve the problem of correlation between local estimates but it gives insight into the statistical effects of the correlation and is working much well in distributed sensor networks.
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