This paper focuses on the convergence rate and numerical characteristics of the nonlinear\ninformation consensus filter for object tracking using a distributed sensor network. To avoid the\nJacobian calculation, improve the numerical characteristic and achieve more accurate estimation\nresults for nonlinear distributed estimation, we introduce square-root extensions of derivative-free\ninformation weighted consensus filters (IWCFs), which employ square-root versions of unscented\ntransform, Stirling�s interpolation and cubature rules to linearize nonlinear models, respectively.\nIn addition, to improve the convergence rate, we introduce the square-root dynamic hybrid consensus\nfilters (DHCFs), which use an estimated factor to weight the information contributions and shows a\nfaster convergence rate when the number of consensus iterations is limited. Finally, compared to the\nstate of the art, the simulation shows that the proposed methods can improve the estimation results\nin the scenario of distributed camera networks.
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