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

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.