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DC Field | Value | Language |
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dc.contributor.author | Date, P | - |
dc.contributor.author | Kumar, G | - |
dc.contributor.author | Pachori, RB | - |
dc.contributor.author | Swaminathan, R | - |
dc.contributor.author | Singh, AK | - |
dc.date.accessioned | 2022-08-26T12:52:02Z | - |
dc.date.available | 2022-08-26T12:52:02Z | - |
dc.date.issued | 2022-08-19 | - |
dc.identifier.citation | Date, P., et. al. (2022) "Wrapped Particle Filtering for Angular Data," in IEEE Access, doi: 10.1109/ACCESS.2022.3200478. | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/25128 | - |
dc.description.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. | en_US |
dc.description.sponsorship | 10.13039/501100001409-Department of Science and Technology, Ministry of Science and Technology, India (Grant Number: CRG/2019/001356 and DST/INSPIRE/04/2018/000089) | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Nonlinear dynamical systems | en_US |
dc.subject | Angular data | en_US |
dc.subject | Particle filtering | en_US |
dc.subject | Wrapped normal distribution | en_US |
dc.subject | Rogers-Szego quadrature rule | en_US |
dc.title | Wrapped Particle Filtering for Angular Data | en_US |
dc.type | Article | en_US |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Accepted | - |
dc.rights.license | This work is licensed under a Creative Commons Attribution 4.0 License. | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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