Time-varying noise statistic estimator based adaptive simplex cubature Kalman filter
type: Item
instance of: scholarly article
title: Time-varying noise statistic estimator based adaptive simplex cubature Kalman filter
zbMATH Open document ID: 1426.93337
DOI: 10.1155/2017/5349879
author: Zhaoming Li
author: Wenge Yang
author: Dan Ding
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: To address the problem that filtering accuracy is reduced with the inaccurate time-varying noise statistic in conventional cubature Kalman filter, a noise statistic estimator based adaptive simplex cubature Kalman filter is put forward in this paper. First, the simplex cubature rule is adopted to approximate the intractable nonlinear Gaussian weighted integral in the filter. Secondly, a suboptimal unbiased constant noise statistic estimator is derived based on the maximum a posteriori estimation criterion. For the time-varying noise, the above estimator is modified using an exponential weighted attenuation method to realize the oblivion of stale data which results in a fading memory estimator, which has the ability to estimate the time-varying noise statistic to revise the filter online. The simulation results indicate that the proposed filter can achieve higher accuracy than conventional filters with inaccurate noise statistic.
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 62M20
zbMATH DE Number: 6972217
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/5349879
OpenAlex ID: W2774586651
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description: scientific article; zbMATH DE number 6972217