Extended target tracking filter with intermittent observations

Jie Shi; Yinya Li; Guoqing Qi; Andong Sheng

Extended target tracking filter with intermittent observations

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Author(s): Jie Shi ¹ ; Yinya Li ¹ ; Guoqing Qi ¹ ; Andong Sheng ¹
- Affiliations: 1: School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, People's Republic of China
Source: Volume 10, Issue 6, August 2016, p. 592 – 602
DOI: 10.1049/iet-spr.2015.0389 , Print ISSN 1751-9675, Online ISSN 1751-9683

Received 10/09/2015, Accepted 18/02/2016, Revised 19/01/2016, Published 01/08/2016

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.

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