A recursive state estimator in the presence of state inequality constraints

Autor:	Mohamed F. Hassan, Hamed M. K. Alazemi, Mohamed Zribi
Rok vydání:	2011
Předmět:	Mathematical optimization Minimum-variance unbiased estimator Efficient estimator Bias of an estimator Control and Systems Engineering Stein's unbiased risk estimate Consistent estimator Trimmed estimator Rao–Blackwell theorem Invariant estimator Computer Science Applications Mathematics
Zdroj:	International Journal of Control, Automation and Systems. 9:237-248
ISSN:	2005-4092 1598-6446
Popis:	This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::accdc65fec1ec9b0bcc5f8be7bb10a27 https://doi.org/10.1007/s12555-011-0205-4 Zobrazit plný text záznamu Full text from SpringerLink