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SCIENCE CHINA Information Sciences, Volume 59, Issue 9: 092203(2016) https://doi.org/10.1007/s11432-015-5488-2

Stochastic stability of cubature predictive filter

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  • ReceivedAug 31, 2015
  • AcceptedDec 16, 2015
  • PublishedAug 23, 2016

Abstract

In this paper, the cubature predictive filter (CPF) is derived based on a third-degree spherical-radial cubature rule. It provides a set of cubature-points scaling linearly with the state-vector dimension, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear predictive filter (PF). In order to facilitate the new method, the algorithm CPF is given firstly. Then, the theoretical analyses demonstrate that the estimated accuracy of the model error and system for the proposed CPF is higher than that of the traditional PF. Moreover, the authors analyze the stochastic boundedness and the error behavior of CPF for general nonlinear systems in a stochastic framework. In particular, the theoretical results present that the estimation error remains bounded and the covariance keeps stable if the system's initial estimation error, disturbing noise terms as well as the model error are small enough, which is the core part of the CPF theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system.


Funded by

National Natural Science Foundation of China(61503414)

"source" : null , "contract" : "2016ADL-DW0202"}]

State Key Laboratory of Astronautic Dynamics Foundation(ADL)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61503414) and State Key Laboratory of Astronautic Dynamics Foundation (ADL) (Grant No. 2016ADL-DW0202).


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