Monte Carlo study of the optimal non-linear estimator: linear systems with non-gaussian initial states †
作者:
S. K. PARK,
D. G. LAINIOTIS,
期刊:
International Journal of Control
(Taylor Available online 1972)
卷期:
Volume 16,
issue 6
页码: 1029-1040
ISSN:0020-7179
年代: 1972
DOI:10.1080/00207177208932335
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The optimal, in the mean-square sense, estimate of state vector of a linear discrete system that is excited by white zero mean gaussian noise and that has non-gaussian initial state vector is presented. Both the optimal estimate and the corresponding error covariance matrix are given. It is shown that the optimal estimator consists of two parts : a linear estimator which is a Kalman filter and a non-linear part which is a parameter estimator. In addition, the a posteriori probability density function, p(x(k)λk), is also given. Finally, a suboptimal procedure that reduces the computational requirements is presented.
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