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2013 (Vol. 5, Issue: 10)
Article Information:

High Maneuvering Target Tracking Based on Self-adaptive Interaction Multiple-Model

Jie Jia, Ke Lu, Jing Wang, Rui Zhai and Yong Yang
Corresponding Author:  Jie Jia 

Key words:  Interactive multiple model, Markov transition probability, Monte Carlo, target tracking, , ,
Vol. 5 , (10): 3063-3068
Submitted Accepted Published
September 27, 2012 November 08, 2012 March 25, 2013
Abstract:

This study establishes a target motion model and an observation model under the condition of colored noise by using the Kalman filter based on an improved IMM (interactive multiple model) for maneuvering target tracking. To improve the overall performance of IMM algorithm, we proposed to combine the CV (constant velocity) and CA (constant acceleration) models with the "current" statistical model, in which its acceleration extremum is not fixed. Since the system model information is implicit in the current measurement, the Markov transition probability is computed online and real-timely, so as to obtain more accurate a posterior estimation and improve the model fusion accuracy. Monte Carlo simulations are carried out for the experiments and the results reveal that the proposed algorithm can get better performance in comparison with traditional IMM which adopts the "current" statistical model and CV-CA models.
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  Cite this Reference:
Jie Jia, Ke Lu, Jing Wang, Rui Zhai and Yong Yang, 2013. High Maneuvering Target Tracking Based on Self-adaptive Interaction Multiple-Model.  Research Journal of Applied Sciences, Engineering and Technology, 5(10): 3063-3068.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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