Likelihood Consensus and Its Application to Distributed Particle Filtering

TL;DR

This paper introduces a distributed likelihood consensus method for wireless sensor networks to perform global state estimation without a central fusion center, enabling efficient distributed particle filtering.

Contribution

It proposes a novel consensus-based approach to approximate the joint likelihood function, facilitating distributed particle filtering in sensor networks with exponential family likelihoods.

Findings

Effective distributed particle filtering demonstrated in simulations

Significant reduction in particles needed for Gaussian filters

Improved accuracy in multi-target tracking scenarios

Abstract

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measurements of all sensors---using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem.