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

Murat Uney, Bernard Mulgrew, Daniel Clark

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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 - 5 Jul 2016
EventFusion 2016 - Heidelberg, Germany
Duration: 5 Jul 20168 Jul 2016


ConferenceFusion 2016

Keywords / Materials (for Non-textual outputs)

  • sensor calibration
  • Markov random fields
  • belief propagation
  • separable likelihoods
  • fusion networks
  • distributed estimation
  • self-localisation


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