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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/933576
- A scenario-based approach to parameter estimation in state-space models having quantized output data
Marelli, Damián E.;
Godoy, Boris I.;
Goodwin, Graham C.
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Engineering
- In this paper we describe an algorithm for estimating the parameters of a linear, discrete-time system, in state-space form, having quantized measurements. The estimation is carried out using the maximum likelihood criterion. The solution is found using the expectation maximization (EM) algorithm. A technical difficulty in applying this algorithm for this problem is that the a posteriori probability density function, found in the EM algorithm, is non-Gaussian. To deal with this issue, we sequentially approximate it using scenarios, i.e., a weighted sum of impulses which are deterministically computed. Numerical experiments show that the proposed approach leads to a significantly more accurate estimation than the one obtained by ignoring the presence of the quantizer and applying standard estimation methods.
- 49th IEEE Conference on Decision and Control (CDC 2010). Proceedings of the 49th IEEE Conference on Decision and Control (Atlanta, GA 15-17 December, 2010) p. 2011-2016
- Publisher Link
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
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