Research of Infrared Compressive Imaging Based Point Target Tracking Method

Li Shao-yi; Liang Shuang; Zhang Kai; Dong Min-zhou; Yan Jie

doi:10.11999/JEIT141324

Volume 37 Issue 7

Jul. 2015

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Li Shao-yi, Liang Shuang, Zhang Kai, Dong Min-zhou, Yan Jie. Research of Infrared Compressive Imaging Based Point Target Tracking Method[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT141324

Citation:

Li Shao-yi, Liang Shuang, Zhang Kai, Dong Min-zhou, Yan Jie. Research of Infrared Compressive Imaging Based Point Target Tracking Method[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1639-1645. doi: 10.11999/JEIT141324

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Research of Infrared Compressive Imaging Based Point Target Tracking Method

doi: 10.11999/JEIT141324

Received Date: 2014-10-15
Rev Recd Date: 2015-01-09
Publish Date: 2015-07-19

Abstract

Abstract

Currently the application research of compressive measurements is still focused on the image recovery, but the ultimate purpose is a task of target detection and tracking in many special applications. And the issue performing target detection and tracking based on compressive measurements is not yet solved. The mapping model is firstly exploited to locate the target in the spatial domain through the measurements in the compressive domain. Further, a method tracking point targets through decoding targets location in the low-dimensional compressive measurements without reconstructed image is proposed for the possible application in space based infrared detection. The method uses the Hadamard matrix to design infrared compressive imaging system, and separates the background and foreground image from the low-dimensional compressive measurements by the adaptive compressive background subtraction. With the mapping relation from the compressive domain into the spatial domain, the target location is possibly decoded. Then the task of point target tracking in the clutter environment can be done by the associated data association and Kalman filtering algorithm. The theoretical analysis and numerical simulations demonstrate the approach proposed is able to accomplish a task of target tracking only by using less compressive measurements, and reduce detector scale, computation complexity and storage cost.
- Target tracking,
- Data association,
- Kalman filtering,
- Compressive imaging,
- Compressive background subtraction

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References(20)

References

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