Online Stochastic Convergence Analysis of the Kalman Filter
Joint Authors
Source
International Journal of Stochastic Analysis
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-21
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper presents modifications to the stochastic stability lemma which is then used to estimate the convergence rate and persistent error of the linear Kalman filter online without using knowledge of the true state.
Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time to monitor the performance of the filter.
Through simulation of an example problem, the new method was shown to be effective in determining a bound on the estimation error that closely follows the actual estimation error.
Different cases of assumed process and measurement noise covariance matrices were considered in order to study their effects on the convergence and persistent error of the Kalman filter.
American Psychological Association (APA)
Rhudy, Matthew B.& Gu, Yu. 2013. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-456528
Modern Language Association (MLA)
Rhudy, Matthew B.& Gu, Yu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-456528
American Medical Association (AMA)
Rhudy, Matthew B.& Gu, Yu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-456528
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-456528