Localization of acoustic sources using a decentralized particle filter
This paper addresses the decentralized localization of an acoustic source in a (wireless) sensor network based on the underlying partial differential equation (PDE). The PDE is transformed into a distributed state-space model and augmented by a source model. Inferring the source state amounts to a non-linear non-Gaussian Bayesian estimation problem for whose solution we implement a decentralized particle filter (PF) operating within and across clusters of sensor nodes. The aggregation of the local posterior distributions from all clusters is achieved via an enhanced version of the maximum consensus algorithm. Numerical simulations illustrate the performance of our scheme.
decentralized particle filter distributed state-space model source localization acoustic wave equation sequential bayesian estimation argumentum-maximi consensus algorithm