A Two-stage Particle Filter in High Dimension

Wenbo Wang, Pranab K. Mandal

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    Particle Filter (PF) is a popular sequential Monte Carlo method to deal with non-linear non-Gaussian filtering problems. However, it suffers from the so-called curse of dimensionality in the sense that the required number of particle (needed for a reasonable performance) grows exponentially with the dimension of the system. One of the techniques found in the literature to tackle this is to split the high-dimensional state in to several lower dimensional (sub)spaces and run a particle filter on each subspace, the so-called multiple particle filter (MPF). It is also well-known from the literature that a good proposal density can help to improve the performance of a particle filter. In this article, we propose a new particle filter consisting of two stages. The first stage derives a suitable proposal density that incorporates the information from the measurements. In the second stage a PF is employed with the proposal density obtained in the first stage. Through a simulated example we show that in high-dimensional systems, the proposed two-stage particle filter performs better than the MPF with much fewer number of particles.
    Original languageEnglish
    Title of host publication20th International Conference on Information Fusion
    Subtitle of host publication2017 Proceedings
    Number of pages8
    ISBN (Electronic)978-0-9964-5270-0
    ISBN (Print)978-1-5090-4582-2
    Publication statusPublished - 2017
    Event20th International Conference on Information Fusion, FUSION 2017 - Xi'an, China
    Duration: 10 Jul 201713 Jul 2017
    Conference number: 20


    Conference20th International Conference on Information Fusion, FUSION 2017
    Abbreviated title FUSION 2017


    • high-dimensional systems
    • Particle filter
    • Optimal proposal


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