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Details for article 11 of 19 found articles
| Title: |
Monte Carlo study of the optimal non-linear estimator: linear systems with non-gaussian initial states † |
| Author: |
Park, S. K. Lainiotis, D. G. |
| Appeared in: |
International journal of control |
| Paging: | Volume 16 (1972) nr. 6 pages 1029-1040 |
| Year: |
1972-12-01 |
| Contents: |
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. The results of extensive digital computer simulations including Monte Carlo study have been presented to establish that the non-linear filter presented here is far superior to the best linear Kalman filter. A practical filter design criterion for utilizing this non-linear filter with reduced data processing requirements is also given. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 11 of 19 found articles
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