A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems

Yong Zhou; Yu Feng Zhang; Ju Zhong Zhang

doi:10.4028/www.scientific.net/AMM.300-301.623

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 300-301A New Adaptive Square-Root Unscented Kalman Filter...

A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems

Abstract:

This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on Square-Root Unscented Kalman Filter (SRUKF), the traditional Maybeck’s estimator is modified and extended to the nonlinear systems, the estimation of square root of the process noise covariance matrix Q or measurement noise covariance matrix R is obtained straightforwardly. Then the positive semi-definiteness of Q or R is guaranteed, some shortcomings of traditional Maybeck’s algorithm are overcome, so the stability and accuracy of the filter is improved greatly.

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Periodical:

Applied Mechanics and Materials (Volumes 300-301)

Pages:

623-626

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.300-301.623

Citation:

Cite this paper

Online since:

February 2013

Authors:

Yong Zhou, Yu Feng Zhang, Ju Zhong Zhang

Keywords:

Adaptive Filtering, Additive Noise, Estimation, Nonlinear System

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