Earticle

초록

영어

As an important nonlinear filter theory, particle filter is a heated issue in domestic and foreign researches. The option of importance density is one of the key steps of particle filter algorithm. The proper option of importance density can minish the negative influence of filter algorithm caused by degeneracy problem. This paper introduces several widely-used options of importance density systemically, and analyzes their features and applied perspectives respectively. The paper also advances a comprehensive method of importance density, analyzes its technical features, explores the adjudgement and improvement of this method based on various performance, and finally puts forward the necessary further study according to the engineer requirements.

목차

Abstract
 1. The Optimum Importance Density Function
 2. Commonly used Methods for Importance Function Generation
  2.1. Use Prior Probability Density as Importance Function
  2.2. Mixed Importance Function Constituted of both Prior and Posterior Probability Density
  2.3. The Annealingprior Probability Density as the Importance Function
 3. Use Filter Algorithm to Design the Importance Function
  3.1. Use EKF to Design the Importance Function
  3.2. Design the Importance Function through UKF
  3.3. Design the Importance Function through Gaussian-Hermite Filter (GHF)
 4. Mixing of Prior Probability Density and UKF to Generate Importance Function
 5. Conclusion
 Acknowledgements
 Reference

키워드

저자

• Liu Lu [ Harbin Institute of Petroleum,150027,Harbin, China ]
• Meng Yang [ Haerbin Engineering University, 150001,Haerbin, China ]
• Shu Geng [ Harbin Institute of Petroleum,150027,Harbin, China ]
• Shu-fen Wang [ Harbin Institute of Petroleum,150027,Harbin, China ]
• Yong-hui Wang [ Harbin Institute of Petroleum,150027,Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605