Open AccessExtended target tracking filter with intermittent observations
Author(s) -
Shi Jie,
Li Yinya,
Qi Guoqing,
Sheng Andong
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2015.0389
Subject(s) - tracking (education) , computer vision , computer science , artificial intelligence , filter (signal processing) , graphics , bernoulli's principle , opengl , tracking system , computer graphics (images) , visualization , psychology , pedagogy , engineering , aerospace engineering
This study addresses the problem of tracking extended target with intermittent observations. Based on practical applications, two Bernoulli distributed random variables are employed to describe the intermittent phenomenon of the positional measurements and the measurements of target extent, respectively. First, a machine vision algorithm is developed to solve the target shape parameters. Then, four sub‐filters are designed according to the received observations and the achieved target shape parameters. The output of the proposed tracking filer can be obtained by the weighted‐confidence fusion of the sub‐filters. Finally, the machine vision algorithm is evaluated by the virtual target images created in OpenGL (Open Graphics Library) and the real images of a moving ship. The performance of the designed tracking filter is compared with the traditional tracking filter. The experiment results show the effectiveness of the machine vision approach; also the Monte‐Carlo runs demonstrate that the provided tracking filter outperforms the traditional one with respect to accuracy.