1973
DOI: 10.1109/tac.1973.1100420
|View full text |Cite
|
Sign up to set email alerts
|

Identification of optimum filter steady-state gain for systems with unknown noise covariances

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
62
0
2

Year Published

2005
2005
2017
2017

Publication Types

Select...
4
4

Relationship

0
8

Authors

Journals

citations
Cited by 132 publications
(65 citation statements)
references
References 7 publications
0
62
0
2
Order By: Relevance
“…Covariance matching is the computation of the covariances from the residuals of the state estimation problem, but has been shown to give biased estimates of the true covariances. The fourth category is correlation techniques, largely pioneered by Mehra [7], [8] and Carew and Bélanger [9], [10]. The correlation method is the most popular and highly cited method for determining these covariances.…”
Section: B Overview Of the Autocovariance Least Squares (Als)mentioning
confidence: 99%
See 2 more Smart Citations
“…Covariance matching is the computation of the covariances from the residuals of the state estimation problem, but has been shown to give biased estimates of the true covariances. The fourth category is correlation techniques, largely pioneered by Mehra [7], [8] and Carew and Bélanger [9], [10]. The correlation method is the most popular and highly cited method for determining these covariances.…”
Section: B Overview Of the Autocovariance Least Squares (Als)mentioning
confidence: 99%
“…Now consider the autocovariance, defined as the expectation of the data with some lagged version of itself [12] (8) Using (5) and the steady-state initial condition (Assumption 1.3) gives for the autocovariance (9) (10) which are independent of because of our assumption about the initial conditions. The symmetric autocovariance matrix (ACM) is then defined as…”
Section: B Overview Of the Autocovariance Least Squares (Als)mentioning
confidence: 99%
See 1 more Smart Citation
“…After the prediction step, we can also define the innovation y k and its covariance matrix S k as y k = z k − H x k|k−1 (12) S k = HP k|k−1 H T + R.…”
Section: Introductionmentioning
confidence: 99%
“…For an optimal filter it can be shown that this sequence should be a white noise (Anderson and Moore, 1979). This property is used in (Mehra, 1970) and (Carew and Bélanger, 1973) to iteratively construct the optimal choices for either Q, R, S or K. Instead of considering the covariance of e(k), it is also possible to use the covariance of the stochastic part of y(k). Then it is possible to directly identify the Kalman gain matrix K without K having to be parameterized.…”
Section: Introductionmentioning
confidence: 99%