Ma Tianlei, Shi Zelin, Yin Jian, Xu Baoshu, Liu Yunpeng. Dim air target detection based on radiation accumulation and space inversion[J]. Infrared and Laser Engineering, 2015, 44(11): 3500-3506.
| Citation:
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Ma Tianlei, Shi Zelin, Yin Jian, Xu Baoshu, Liu Yunpeng. Dim air target detection based on radiation accumulation and space inversion[J]. Infrared and Laser Engineering, 2015, 44(11): 3500-3506.
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Dim air target detection based on radiation accumulation and space inversion
- 1.
Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;
- 2.
Key Laboratory of Optical-Electronics Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;
- 3.
University of Chinese Academy of Sciences,Beijing 100049,China;
- 4.
The Research Institute on General Development and Argumentation of Equipment of Air Force,Beijing 100076,China
- Received Date: 2015-03-05
- Rev Recd Date:
2015-04-03
- Publish Date:
2015-11-25
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Abstract
Background radiation noise interference is a difficult technical problem for dim signal detection. A dim target detection algorithm was proposed which can significantly improve signal-to-noise ratio(SNR) to achieve uniformly motion dim target detection successfully. Firstly, a coordinate space and a velocity space were established. Then the original image sequence was stacked along different velocity vectors to acquire a new image sequence with SNR improved and the new image sequence forms an image space. Secondly, quasi-target points in the image space were detected by constant false-alarm ratio(CFAR) judging. Finally, velocity vectors and coordinate vectors of quasi-target points were mapped to the velocity space and the coordinate space respectively. As a result, two local peaks from the spaces will confirm true target points.Experiments of real images from actual IR imaging system show that proposed algorithm can improve SNR approximately up to N times of original image SNR, and the proposed algorithm is demonstrably superior to compared algorithms on detection probability and false alarm probability.
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