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Synthetically adaptive robust filtering for satellite orbit determination

Science China Earth Sciences 2004年第7期47卷 585-592页

作者： YANG Yuanxi1 & WEN Yuanlan2 1. Xi’an Research Institute of Surveying and Mapping, Xi抋’an 710054, China 2. College of Aerospace and Material Engineering, National University of Defense Technology, Changsha 410073, China Xi’an Research Institute of Surveying and Mapping Xi’an China College of Aerospace and Material Engineering National University of Defense Technology Changsha China

The quality of the satellite orbit determination is rested on the knowledge of per-turbing forces acting on the satellite and stochastic properties of the observations, and the ability of controlling various kinds of errors. After a brief discussion on the dynamic and geometric orbit determinations, Sage adaptive filtering and robust filtering are reviewed. A new synthetically adaptive robust filtering based on a combination of robust filtering and Sage filtering is devel-oped.It is shown, by derivations and calculations, that the synthetically adaptive robust filtering for orbit determination is not only robust but also simple in calculation. It controls the effects of the outliers of tracking observations and the satellite dynamical disturbance on the parameter esti-mates of the satellite orbit.

关键词： satellite orbit, Sage filtering, adaptive orbit determination, robust estimation.

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Stochastic Iterative Learning Control With Faded Signals

引用

IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1196-1208页

作者： Ganggui Qu Dong Shen the College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China

Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.

关键词： Fading channels iterative learning control (ILC) Kalman filtering, mean-square convergence stochastic systems

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