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http://bura.brunel.ac.uk/handle/2438/25128
Title: | Wrapped Particle Filtering for Angular Data |
Authors: | Date, P Kumar, G Pachori, RB Swaminathan, R Singh, AK |
Keywords: | Nonlinear dynamical systems;Angular data;Particle filtering;Wrapped normal distribution;Rogers-Szego quadrature rule |
Issue Date: | 19-Aug-2022 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Date, P., et. al. (2022) "Wrapped Particle Filtering for Angular Data," in IEEE Access, doi: 10.1109/ACCESS.2022.3200478. |
Abstract: | Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution. In this paper, we propose using a wrapped normal distribution as a proposal distribution for angular data, i.e. data within finite range (-π,π]. We then use the same method to derive the proposal density for a particle filter, in place of a standard assumed Gaussian density filter such as the unscented Kalman filter. The numerical integrals with respect to wrapped normal distribution are evaluated using Rogers-Szegő quadrature. Compared to using the unscented filter and similar approximate Gaussian filters to produce proposal densities, we show through examples that wrapped normal distribution gives a far better filtering performance when working with angular data. In addition, we demonstrate the trade-off involved in particle filters with local sampling and global sampling (i.e. by running a bank of approximate Gaussian filters vs running a single approximate Gaussian filter) with the former yielding a better filtering performance than the latter at the cost of increased computational load. |
URI: | http://bura.brunel.ac.uk/handle/2438/25128 |
ISSN: | 2169-3536 |
Appears in Collections: | Dept of Mathematics Research Papers |
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