Distributed estimation of latent parameters in state space models using separable likelihoods

Murat Uney, Bernard Mulgrew, Daniel E Clark

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

2 Citations (Scopus)

Abstract

Motivated by object tracking applications with networked sensors, we consider multi sensor state space models. Estimation of latent parameters in these models requires centralisation because the parameter likelihood depend on the measurement histories of all of the sensors. Consequently, joint processing of multiple histories pose difficulties in scaling with the number of sensors. We propose an approximation with a node-wise separable structure thereby removing the need for centralisation in likelihood computations. When leveraged with Markov random field models and message passing algorithms for inference, these likelihoods facilitate decentralised estimation in tracking networks as well as scalable computation schemes in centralised settings. We establish the connection between the approximation quality of the proposed separable likelihoods and the accuracy of state estimation based on individual sensor histories. We demonstrate this approach in a sensor network self-localisation example.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages4129-4133
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 19 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Publication series

NameIEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Print)2379-190X

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • hidden Markov models
  • Markov random fields
  • pseudo-likelihood
  • sensor networks
  • simultaneous localisation and tracking

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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  • Cite this

    Uney, M., Mulgrew, B., & Clark, D. E. (2016). Distributed estimation of latent parameters in state space models using separable likelihoods. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4129-4133). [7472454] (IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2016.7472454