Cooperative sensor localisation in distributed fusion networks by exploiting non-cooperative targets

Murat Üney, Bernard Mulgrew, Daniel Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


We consider geographically dispersed and networked sensors collecting measurements from multiple targets in a surveillance region. Each sensor node filters the set of cluttered, noisy target measurements it collects in a sensor centric coordinate system and with imperfect detection rates. The filtered multi-target information is, then, communicated to the nearest neighbours. We are interested in network self-localisation in scenarios in which the network is restricted to use only the multi-target information shared. We propose an online distributed sensor localisation scheme based on a pairwise Markov Random Field model of the problem. We first introduce parameter likelihoods for pairs of sensors-equivalently, edge potentials-which can be computed using only the incoming multi-target information and local measurements. Then, we use belief propagation with the associated posterior model which is Markov with respect to the underlying communication topology. We demonstrate the efficacy of our algorithm for cooperative sensor localisation through an example with complex measurement models.

Original languageEnglish
Title of host publicationIEEE Workshop on Statistical Signal Processing Proceedings
Number of pages4
ISBN (Print)9781479949755
Publication statusPublished - 2014
Event17th IEEE Workshop on Statistical Signal Processing 2014 - Gold Coast, Australia
Duration: 29 Jun 20142 Jul 2014


Conference17th IEEE Workshop on Statistical Signal Processing 2014
Abbreviated titleSSP 2014
CityGold Coast


  • cooperative localisation
  • graphical models
  • Monte Carlo algorithms
  • multi-target tracking
  • sensor networks

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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