Earticle

초록

영어

Firstly, through the principle analysis and simulation experiment, the maneuvering target tracking algorithm of curve model interacting multiple model tracking algorithm was given. Because the algorithm is simple structure and high cost efficiency, it becomes generally applicable algorithm for the curve tracking model. But, the target mobility is very high in practice, Single target tracking model is no longer applicable curve tracking model. To improve the accuracy of tracking, the adaptive grid interacting multiple model (AGIMM) algorithm was given. The algorithm has two fatal weaknesses in the practical application. First, in maneuvering target tracking process, when the model changes and gradual change, the tracking precision is not high; Second, because the changing model structure is very large model sets, the algorithm is complexity and system processing speed is very slow, which cannot be widely used. To improve the accuracy and its scope of application of the algorithm, The paper proposed the adaptive Kalman filter adaptive interacting multiple model algorithm (AKFAIMM).The algorithm introduced the parameter in the adaptive Kalman filter, and adjusted parameter in maneuvering target tracking, the parameter was adjusted continuously in the curve motion model, it could greatly improve the tracking precision and the application of the model. Second, to improve the algorithm complexity. The paper improved that the angular velocity estimation method replaced centripetal acceleration estimation method on turning curve. The estimation method reduced the number of model set and reduced greatly of computation. At the same time, according to the algorithm in the model changes, the centripetal acceleration could be continuously adjusted and improved the adaptability of the model. The algorithm improved maneuvering target tracking algorithm accuracy. The effectiveness of algorithm was proved the validity by simulation.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Maneuvering Target Modeling
  2.2. The Principle of the Algorithm IMM
 3. An Improved Kalman Algorithm
  3.1. Improved Motion Equation
  3.2. Simulation Comparison for the Improved Filter Model
 4. Adaptive Kalman Filtering with Adaptive Interactive Multiple Model Tracking Algorithm
  4.1. The Principle of the Improved Algorithm and Steps
  4.2. The Theoretical Analysis of Turn Model
  4.3. Adaptive Interacting Multiple Model Tracking Model (AIMM)
  4.4. The Improved Algorithm Simulation
 5. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Xiuling He [ Institute of Disaster Prevention HeBei, SanHe 065201, China ]
• Jingsong Yang [ Institute of Disaster Prevention HeBei, SanHe 065201, China ]
• Tao Li [ The 28th Research Institute of China Electronics Technology Group Corporation, JiangSu, NanJing 210007, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605