Benavoli, Alessio (2013) The Generalized Moment-Based Filter. IEEE Transactions on Automatic Control, 58 (10). pp. 2642-2647. ISSN 0018-9286
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Abstract
Can we solve the filtering problem from the only knowledge of few moments of the noise terms? In this paper, by exploiting set of distributions based filtering, we solve this problem without introducing additional assumptions on the distributions of the noises (e.g., Gaussianity) or on the final form of the estimator (e.g., linear estimator). Given the moments (e.g.,mean and variance) of random variable X, it is possible to define the set of all distributions that are compatible with the moments information. This set can be equivalently characterized by its extreme distributions: a family of mixtures of Dirac’s deltas. The lower and upper expectation of any function g of X are obtained in correspondence of these extremes and can be computed by solving a linear programming problem. The filtering problem can then be solved by running iteratively this linear programming problem. In this paper, we discuss theoretical properties of this filter, we show the connection with set-membership estimation and its practical applications.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
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| Uncontrolled Keywords: | Generalized moments;Kalman filter;robustness;set of distributions;set-membership estimation |
| Subjects: | Engineering > Electronic & electrical engineering > Control systems |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
| Depositing User: | Alessio Benavoli |
| Date Deposited: | 13 Mar 2014 14:08 |
| Last Modified: | 23 May 2016 12:49 |
| URI: | http://repository.supsi.ch/id/eprint/3849 |
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