Journal of Applied Mathematics

  • J. Appl. Math.
  • Volume 2014, Special Issue (2014), Article ID 139503, 10 pages.

Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

Hongjian Wang, Jinlong Xu, Aihua Zhang, Cun Li, and Hongfei Yao

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Abstract

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.

Article information

Source
J. Appl. Math., Volume 2014, Special Issue (2014), Article ID 139503, 10 pages.

Dates
First available in Project Euclid: 27 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425051931

Digital Object Identifier
doi:10.1155/2014/139503

Mathematical Reviews number (MathSciNet)
MR3216116

Citation

Wang, Hongjian; Xu, Jinlong; Zhang, Aihua; Li, Cun; Yao, Hongfei. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems. J. Appl. Math. 2014, Special Issue (2014), Article ID 139503, 10 pages. doi:10.1155/2014/139503. https://projecteuclid.org/euclid.jam/1425051931


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