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Optical Engineering

Rectilinear-motion space inversion-based detection approach for infrared dim air targets with variable velocities
Author(s): Tianlei Ma; Zelin Shi; Jian Yin; Yunpeng Liu; Baoshu Xu; Chengshuo Zhang
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Paper Abstract

Dim targets are extremely difficult to detect using methods based on single-frame detection. Radiation accumulation is one of the effective methods to improve signal-to-noise ratio (SNR). A detection approach based on radiation accumulation is proposed. First, a location space and a motion space are established. Radiation accumulation operation, controlled by vectors from the motion space, is applied to the original image space. Then, a new image space is acquired where some images have an improved SNR. Second, quasitargets in the new image space are obtained by constant false-alarm ratio judging, and location vectors and motion vectors of quasitargets are also acquired simultaneously. Third, the location vectors and motion vectors are mapped into the two spaces, respectively. Volume density function is defined in the motion space. Location extremum of the location space and volume density extremum of motion space will confirm the true target. Finally, actual location of the true target in the original image space is obtained by space inversion. The approach is also applicable to detect multiple dim targets. Experimental results show the effectiveness of the proposed approach and demonstrate the approach is superior to compared approaches on detection probability and false alarm probability.

Paper Details

Date Published: 11 March 2016
PDF: 14 pages
Opt. Eng. 55(3) 033102 doi: 10.1117/1.OE.55.3.033102
Published in: Optical Engineering Volume 55, Issue 3
Show Author Affiliations
Tianlei Ma, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Zelin Shi, Shenyang Institute of Automation (China)
Jian Yin, Research Institute on General Development and Argumentation of Equipment of Air Force (China)
Yunpeng Liu, Shenyang Institute of Automation (China)
Baoshu Xu, Shenyang Institute of Automation (China)
Chengshuo Zhang, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)

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