1. Consensus-based multiple-model Bayesian filtering for distributed
tracking
Abstract:
This study addresses distributed state estimation of jump Markovian
systems and its application to tracking of a manoeuvring target by means
of a network of heterogeneous sensors and communication nodes. Two
novel consensus-based multiple-model filters are presented. Simulation
experiments in a tracking case study, involving a strongly manoeuvring
target and a sensor network characterised by weak connectivity,
demonstrate the superiority of the proposed distributed multiple-mode
filters with respect to existing solutions.