Earticle

초록

영어

The unscented Kalman filter (UKF) has become a popular method for nonlinear state estimation during the last decade. However, the conventional UKF may not be suitable for real-world applications with state constrains that stem from physical definitions, physical laws or model restrictions. A UKF based method with optimized parameters was proposed in this paper to handle state constraints via the projection of sigma points. In the proposed method, the generated sigma points that violate the state constraints were projected onto the constraint boundary first. The three free parameters of the UKF, i.e., α ,β ,κ , were then optimized using a Gaussian process optimization (GPO) method. Simulations indicate that the proposed optimized UKF algorithm with the projection of sigma points can handle constrained state estimation problem effectively and efficiently.

목차

Abstract
 1. Introduction
 2. Brief Review of the Generic UKF
 3. Constraints Handling in UKF
  3.1. Sigma Points Projection
  3.2. Sigma Points Scaling
  3.3. Sigma Points Re-Weighting
 4. Parameter Learning for Projection based UKF with GPO
  4.1. GPO based Parameters Learning for the Generic UKF
  4.2. Parameters Learning for Constrained UKF
 5. Constrained State Estimation Simulations
 6. Conclusions
 References

키워드

저자

• Yuanyuan Liu [ Hangzhou Dianzi University, Hangzhou, China ]
• Jingbiao Liu [ Hangzhou Dianzi University, Hangzhou, China ]
• Zhiwei He [ Hangzhou Dianzi University, Hangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605