Earticle

초록

영어

In this paper, we investigate the state estimation problem of nonlinear systems under the condition that the prior statistical characteristic of noise is unknown. An adaptive unscented Kalman filter (UKF) is proposed. In this algorithm, the maximum likelihood principle is applied to establish the log likelihood function with the unknown noise statistical characteristics. Then, the noise property estimation problem is transformed into the maximization of the mean of the log likelihood function, which can be achieved by using the expectation maximization algorithm. Finally, a suboptimal adaptive UKF can be obtained. Simulations show that the proposed adaptive UKF algorithm can deal with the problem of filtering accuracy declination of the traditional UKF when the prior noise statistical characteristic is unknown. The proposed algorithm can estimate the statistical parameters online.

목차

Abstract
 1. Introduction
 2. Problem Formulation and Traditional UKF
 3. Noise Statistics Estimator Based on Maximum Likelihood Principle
 4. Simulations and Analysis
 5. Conclusions
 References

키워드

저자

• Li Guo [ School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024 China, School of Information Science and Engineering, Dalian Polytechnic University, Dalian, Liaoning 116034 China ] Corresponding author
• De-gen Huang [ School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning 116024 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605