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

Murat Uney, Bernard Mulgrew, Daniel Clark

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

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
Number of pages6
Publication statusPublished - 20 Mar 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - China, Shanghai, China
Duration: 20 Mar 201625 Mar 2016
https://www2.securecms.com/ICASSP2016/Default.asp

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated titleICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16
Internet address

Keywords / Materials (for Non-textual outputs)

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

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