Earticle

초록

영어

STUKF (Strong tracking UKF) algorithm uses the time-varying fading factor to fade the past data and reduce the impact on current filter value, thus achieves the goal of adjusting the filter gain matrix in real time. But STUKF algorithm needs three UT for each filtering, and compared with the UKF filter, calculating amount of three UT increases seriously, and it is not conducive to application of engineering, therefore this paper presents an improved STUKF algorithm. Compared with the traditional STUKF filter, this new algorithm introduces the formulas of redefined fading factor. By changing the position of the fading factor, it improves the accuracy and robustness of the algorithm and reduces the computational complexity of the algorithm. Finally simulation results show that the new algorithm has higher precision and stronger robustness.

목차

Abstract
 1. Introduction
 2. Unscented Kalman Filter
  2.1. Problem Description
  2.2. UKF Algorithm
 3. Improved Strong Tracking UKF Algorithm
  3.1. Strong Tracking Filter (STF)
  3.2. STUKF Algorithm
  3.3. ISTUKF Algorithm
 4. GPS/DR Integrated Navigation System Model
 5. Simulation Experiment
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Jian Feng [ School of Computer and Information Engineering Harbin University of Commerce Harbin, China ]
• Xiao-dong Su [ School of Computer and Information Engineering Harbin University of Commerce Harbin, China ]
• Yu-ru Zhang [ School of Computer and Information Engineering Harbin University of Commerce Harbin, China ]
• Hai-tao Jiang [ School of Computer and Information Engineering Harbin University of Commerce Harbin, China ]
• Jun-ling Li [ School of Computer and Information Engineering Harbin University of Commerce Harbin, 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