UKF-based input-output estimation of nonlinear systems in extended noise environment

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作者：
School of Information Science and Engineering, Northeastern University, Shenyang 110004, China [1 ]
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来源：
Dongbei Daxue Xuebao | 2009年 / 7卷 / 923-927期
关键词：
Problem solving - Monte Carlo methods - Noise pollution - Kalman filters - Intelligent systems;
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摘要：
Introduces an extended environment in the unscented Kalman filtering (UKF) algorithm involving the additional noise during input observation to solve the problem of optimal estimation of noise-polluted input/output sequence in nonlinear systems. In such an environment either the errors-in-variables (EIV) filtering algorithm or the UKF algorithm is designed for sub-cases, where the former is used to solve the problem of optimal estimation of noise-polluted input/output sequence, and the latter is to solve the same problem with the additional noise during input observation considered. The evaluation of the expected performance of the UKF algorithm is then analyzed through error covariances. A Monte Carlo simulation shows that the performance of the UKF algorithm leads to the expected validity to make variance minimal.
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