A Cooperative Approach to Sensor Localisation in Distributed Fusion Networks

Murat Uney, Bernie Mulgrew, Daniel Clark

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We consider self-localisation of networked sensor platforms, which are located disparately and collect cluttered and noisy measurements from an unknown number of objects (or, targets). These nodes perform local filtering of their measurements and exchange posterior densities of object states over the network to improve upon their myopic performance. Sensor locations need to be known, however, in order to register the incoming information in a common coordinate frame for fusion. In this work, we are interested in scenarios in which these locations need to be estimated solely based on the multi-object scene. We propose a cooperative scheme which features nodes using only the information they already receive for distributed fusion: we first introduce node-wise separable parameter likelihoods for sensor pairs, which are recursively updated using the incoming multi-object information and the local measurements. Second, we establish a network coordinate system through a pairwise Markov random field model which has the introduced likelihoods as its edge potentials. The resulting algorithm consists of consecutive edge potential updates and Belief Propagation message passing operations. These potentials are capable of incorporating multi-object information without the need to find explicit object-measurement associations and updated in linear complexity with the number of measurements. We demonstrate the efficacy of our algorithm through simulations with multiple objects and complex measurement models.
Original languageEnglish
Pages (from-to)1187-1199
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number5
Early online date26 Oct 2015
Publication statusPublished - 1 Mar 2016

Keywords / Materials (for Non-textual outputs)

  • cooperative localisation
  • multi-target tracking
  • simultaneous localisation and tracking
  • sensor networks
  • graphical models
  • dynamical Markov Random fields


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