Distributed localisation of sensors with partially overlapping field-of-views in fusion networks

Murat Uney, Bernard Mulgrew, Daniel E Clark

Research output: Contribution to conferencePaperpeer-review

12 Citations (Scopus)


We consider geographically distributed sensor platforms with limited field of views (FoVs) networked together in order to cover a larger surveillance region. Each sensor has a partially overlapping FoV with its neighbours, and, collects both target originated and spurious measurements. We are interested in estimating the locations of the sensors in a network coordinate system using only these measurements. The parameter likelihood of the problem, however, does not scale with the number of sensors as its evaluation requires joint multi-sensor filtering. We propose an approximate likelihood which provides scalability by building upon local single sensor filtering, and, is capable of handling partially overlapping coverage for a pair of sensors. Such scalable approximations for fully overlapping sensor coverages have been recently introduced in a cooperative self-calibration framework in which they are used with pairwise Markov random fields as edge potentials. We use the proposed likelihoods within this framework for distributed self-localisation of sensors in the partially overlapping FoVs case. We provide explicit formulae for the likelihoods and a Monte Carlo algorithm which consists of consecutive likelihood updates and belief propagation steps for estimation -all performed as distributed message passings across the network. We demonstrate the estimation accuracy achieved through simulations with multiple objects and complex measurement models.

Original languageEnglish
Number of pages8
Publication statusPublished - 4 Aug 2016
Event19th International Conference on Information Fusion 2016 - Heidelberg, Germany
Duration: 5 Jul 20168 Jul 2016


Conference19th International Conference on Information Fusion 2016
Abbreviated titleFUSION 2016

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing


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