Interlaced sigma-point information filtering for distributed state estimation of multi-agent systems

Abstract

This paper presents a new interlaced sigma-point information filtering (ISPIF) algorithm for distributed state estimation of multi-agent systems (MAS). The ISPIF is derived by first presenting an interlaced information filtering (HF) algorithm for linear MAS and then embedding the sigma-point transformation (SPT) that used in the sigma-point Kalman filters into the IIF architecture through a statistical linear regression methodology. The ISPIF enjoys both the effectiveness brought by the interlacement technique and the accuracies and flexibilities brought by the SPTs. Performance comparison of the ISPIF with the interlaced extended information filter is demonstrated through network localization simulations.