Efficient Monte Carlo Filtering for Discretely Observed Jumping Processes

  • Authors:

  • Nick Whiteley;Adam M. Johansen;Simon Godsill

  • Affiliations:

  • University of Cambridge, Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ, UK;University of Bristol, Department of Mathematics, University Walk, Bristol, BS8 1TW, UK;University of Cambridge, Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ, UK

  • Venue:
  • SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
  • Year:
  • 2007

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Abstract

This paper addresses a tracking problem in which the unobserved process is characterised by a collection of random jump times and associated random parameters. We construct a scheme for obtaining particle approximations to the posterior distributions of interest in the framework of sequential Monte Carlo (SMC) samplers [1]. We describe efficient sampling schemes and demonstrate that two existing schemes can be interpreted as particular cases of the proposed method. Results are provided which illustrate the performance improvements possible with our approach.