Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/16220
Title: | Adaptive Sparse-grid Gauss-Hermite Filter |
Authors: | Date, P Singh, A Bhaumik, S Radhakrishnan, R |
Keywords: | Nonlinear filtering;;Gauss–Hermite quadrature rule;;Product rule;Smolyak rule;;Complexity reduction;;Adaptive sparse-grid |
Issue Date: | 2018 |
Publisher: | Elsevier |
Citation: | Journal of Computational and Applied Mathematics, 2018 |
Abstract: | In this paper, a new nonlinear filter based on sparse-grid quadrature method has been proposed. The proposed filter is named as adaptive sparse-grid Gauss–Hermite filter (ASGHF). Ordinary sparse-grid technique treats all the dimensions equally, whereas the ASGHF assigns a fewer number of points along the dimensions with lower nonlinearity. It uses adaptive tensor product to construct multidimensional points until a predefined error tolerance level is reached. The performance of the proposed filter is illustrated with two nonlinear filtering problems. Simulation results demonstrate that the new algorithm achieves a similar accuracy as compared to sparse-grid Gauss–Hermite filter (SGHF) and Gauss–Hermite filter (GHF) with a considerable reduction in computational load. Further, in the conventional GHF and SGHF, any increase in the accuracy level may result in an unacceptably high increase in the computational burden. However, in ASGHF, a little increase in estimation accuracy is possible with a limited increase in computational burden by varying the error tolerance level and the error weighting parameter. This enables the online estimator to operate near full efficiency with a predefined computational budget. |
URI: | http://bura.brunel.ac.uk/handle/2438/16220 |
ISSN: | 0377-0427 |
Appears in Collections: | Dept of Mathematics Embargoed Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | Item is embargoed but available from 2019-05-07 | 1.11 MB | Adobe PDF | View/Open |
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