Square-Root Sigma-Point Information Consensus Filters for Distributed Nonlinear EstimationReport as inadecuate


Square-Root Sigma-Point Information Consensus Filters for Distributed Nonlinear Estimation


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School of Control Science and Engineering, Shandong University, Jinan 250061, China





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Academic Editor: Yuh-Shyan Chen

Abstract This paper focuses on the convergence rate and numerical characteristics of the nonlinear information consensus filter for object tracking using a distributed sensor network. To avoid the Jacobian calculation, improve the numerical characteristic and achieve more accurate estimation results for nonlinear distributed estimation, we introduce square-root extensions of derivative-free information weighted consensus filters IWCFs, which employ square-root versions of unscented transform, Stirling’s interpolation and cubature rules to linearize nonlinear models, respectively. In addition, to improve the convergence rate, we introduce the square-root dynamic hybrid consensus filters DHCFs, which use an estimated factor to weight the information contributions and shows a faster convergence rate when the number of consensus iterations is limited. Finally, compared to the state of the art, the simulation shows that the proposed methods can improve the estimation results in the scenario of distributed camera networks. View Full-Text

Keywords: target tracking; sensor network; information filter; distributed estimation target tracking; sensor network; information filter; distributed estimation





Author: Guoliang Liu * and Guohui Tian

Source: http://mdpi.com/



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