Earticle

초록

영어

In this paper, an adaptive Kalman Filtering method is presented for the state prediction of random systems. It is shown that the adaptive Kalman Filtering method in conjunction with equilibrium optimization solution can estimate the initial accelerations of targets effectively since the equilibrium optimization solution tunes the state prediction vector to diminish the error between measured value and prediction estimation value. We evaluate our model on special and random trajectories. Experimental evidence shows that the proposed method can robustly estimate an initial acceleration from a dynamic model and stably track a trajectory which is moving randomly.

목차

Abstract
 1. Introduction
 2. Tracking Algorithm
  2.1. Linear Kalman Filtering for Discrete-Time Systems
  2.2. Target Tracking Algorithm
 3. Experiments Test
  3.1. Non-maneuvering Trajectory Tracking Simulations
  3.2. Random Trajectory Tracking Simulation
 4. Conclusion
 References

키워드

저자

• Le Zhang [ Opto-Electronic Engineering Department, Changchun University of Science and Technology, Changchun, China / Qin Dai ]
• Zhiyong An [ Opto-Electronic Engineering Department, Changchun University of Science and Technology, Changchun, China ]
• Yongping Hao [ Equipment Engineering Department, Shenyang Ligong University, Shenyang, China ]
• Qin Dai [ Equipment Engineering Department, Shenyang Ligong University, Shenyang, 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