Abstract
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 language | English |
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Title of host publication | 20th International Conference on Information Fusion |
Subtitle of host publication | 2017 Proceedings |
Publisher | IEEE |
Number of pages | 8 |
ISBN (Electronic) | 978-0-9964-5270-0 |
ISBN (Print) | 978-1-5090-4582-2 |
DOIs | |
Publication status | Published - 2017 |
Event | 20th International Conference on Information Fusion, FUSION 2017 - Xi'an, China Duration: 10 Jul 2017 → 13 Jul 2017 Conference number: 20 |
Conference
Conference | 20th International Conference on Information Fusion, FUSION 2017 |
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Abbreviated title | FUSION 2017 |
Country/Territory | China |
City | Xi'an |
Period | 10/07/17 → 13/07/17 |
Keywords
- high-dimensional systems
- Particle filter
- Optimal proposal