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Avtomatika i Telemekhanika, 2016, Issue 1, Pages 30–49
(Mi at14348)
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This article is cited in 1 scientific paper (total in 1 paper)
Topical issue
Linear filtering with adaptive adjustment of the disturbance covariation matrices in the plant and measurement noise
A. E. Barabanov St. Petersburg State University, St. Petersburg, Russia
Abstract:
For filtering a nonstationary linear plant under the unknown intensities of input signals such as plant disturbances and measurement noise, a new algorithm was presented. It is based on selecting the vectors of values of these signals compatible with the observed plant output and minimizing the error variances of the last predicted measurement. The measurement prediction is determined from the Kalman filter where the input signals are assumed to be white noise and the covariance matrix coincides with the empirical covariance matrix of the selected vectors. Numerical modeling demonstrated that the so-calculated filter coefficients are close to the optimal ones constructed from the true covariance matrices of plant disturbances and measurement noise. The approximate Newton method for minimization of the prediction error variance was shown to agree with the solution of the auxiliary optimal control problem, which allows to make one or some few iterations to find the point of minimum.
Citation:
A. E. Barabanov, “Linear filtering with adaptive adjustment of the disturbance covariation matrices in the plant and measurement noise”, Avtomat. i Telemekh., 2016, no. 1, 30–49; Autom. Remote Control, 77:1 (2016), 21–36
Linking options:
https://www.mathnet.ru/eng/at14348 https://www.mathnet.ru/eng/at/y2016/i1/p30
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Statistics & downloads: |
Abstract page: | 429 | Full-text PDF : | 290 | References: | 86 | First page: | 70 |
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