Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems
Joint Authors
Li, Cun
Xu, Jinlong
Wang, Hongjian
Zhang, Aihua
Yao, Hongfei
Source
Journal of Applied Mathematics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-25
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
We present a support vector regression-based adaptive divided difference filter (SVRADDF) algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises.
The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations.
The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence.
Support vector regression (SVR) is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness.
The performance of the proposed algorithm is evaluated by estimating states for (i) an underwater nonmaneuvering target bearing-only tracking system and (ii) maneuvering target bearing-only tracking in an air-traffic control system.
The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
American Psychological Association (APA)
Wang, Hongjian& Xu, Jinlong& Zhang, Aihua& Li, Cun& Yao, Hongfei. 2014. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems. Journal of Applied Mathematics،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-448869
Modern Language Association (MLA)
Wang, Hongjian…[et al.]. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems. Journal of Applied Mathematics No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-448869
American Medical Association (AMA)
Wang, Hongjian& Xu, Jinlong& Zhang, Aihua& Li, Cun& Yao, Hongfei. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems. Journal of Applied Mathematics. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-448869
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-448869