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JOURNAL OF RADARS  2014, Vol. 3 Issue (5): 497-504    DOI: 10.3724/SP.J.1300.2014.14092
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Low-resolution Airborne Radar Air/ground Moving Target Classification and Recognition
Wang Fu-you①② Luo Ding Liu Hong-wei
(AVIC LEIHUA Electronic Technology Research Institute, Wuxi 214063, China)
(National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China)
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Abstract Radar Target Recognition (RTR) is one of the most important needs of modern and future airborne surveillance radars, and it is still one of the key technologies of radar. The majority of present algorithms are based on wide-band radar signal, which not only needs high performance radar system and high target Signal-to-Noise Ratio (SNR), but also is sensitive to angle between radar and target. Low-Resolution Airborne Surveillance Radar (LRASR) in downward-looking mode, slow flying aircraft and ground moving truck have similar Doppler velocity and Radar Cross Section (RCS), leading to the problem that LRASR air/ground moving targets can not be distinguished, which also disturbs detection, tracking, and classification of low altitude slow flying aircraft to solve these issues, an algorithm based on narrowband fractal feature and phase modulation feature is presented for LRASR air/ground moving targets classification. Real measured data is applied to verify the algorithm, the classification results validate the proposed method, helicopters and truck can be well classified, the average discrimination rate is more than 89% when SNR ≥ 15 dB.
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Wang Fu-you
Luo Ding
Liu Hong-wei
Key wordsLow-resolution airborne radar   Air/ground moving target classification   Fractal feature   Phase modulation feature   Support Vector Machine (SVM)     
Received: 2014-06-11; Published: 2014-07-14
Cite this article:   
Wang Fu-you,Luo Ding,Liu Hong-wei. Low-resolution Airborne Radar Air/ground Moving Target Classification and Recognition[J]. JOURNAL OF RADARS, 2014, 3(5): 497-504.
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[1] Wang Fu-you, Luo Ding, Liu Hong-wei. Low-resolution Airborne Radar Aircraft Target Classification[J]. JOURNAL OF RADARS, 2014, 3(4): 444-449.

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