Thiyagalingam, J ORCID: 0000-0002-2167-1343, Kekempanos, L and Maskell, S ORCID: 0000-0003-1917-2913
(2017)
MapReduce particle filtering with exact resampling and deterministic runtime.
Eurasip Journal on Advances in Signal Processing, 2017 (1).
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Abstract
Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles increases, it is natural to consider as many particles as possible. MapReduce is a generic programming model that makes it possible to scale a wide variety of algorithms to Big data. However, despite the application of particle filters across many domains, little attention has been devoted to implementing particle filters using MapReduce. In this paper, we describe an implementation of a particle filter using MapReduce. We focus on a component that what would otherwise be a bottleneck to parallel execution, the resampling component. We devise a new implementation of this component, which requires no approximations, has O(N) spatial complexity and deterministic O((logN)2) time complexity. Results demonstrate the utility of this new component and culminate in consideration of a particle filter with 224 particles being distributed across 512 processor cores.
Item Type: | Article |
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Additional Information: | 31 pages, 16 figures |
Uncontrolled Keywords: | MCMC methods, Particle filters, Big data sampling, MapReduce, Resampling |
Depositing User: | Symplectic Admin |
Date Deposited: | 11 Oct 2017 13:29 |
Last Modified: | 19 Jan 2023 06:53 |
DOI: | 10.1186/s13634-017-0505-9 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3009952 |
Available Versions of this Item
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MapReduce Particle Filtering with Exact Resampling and Deterministic Runtime. (deposited 21 Sep 2017 08:31)
- MapReduce particle filtering with exact resampling and deterministic runtime. (deposited 11 Oct 2017 13:29) [Currently Displayed]