Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

TL;DR

This paper introduces decentralized algorithms for sensor self-localization and target tracking in sensor networks, using maximum likelihood and EM methods, applicable to linear and non-linear models with demonstrated effectiveness.

Contribution

It presents novel decentralized algorithms for simultaneous sensor localization and target tracking, extending to non-linear models with local linearization techniques.

Findings

Algorithms successfully learn localization parameters in simulations.

Distributed Kalman filter and message passing enable exact implementation for linear models.

Proposed methods adapt to non-linear models via local linearization.

Abstract

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters.