Aircraft Target Classification and Recognition Algorithm Based on Measured Data

Ming LI; Jiaojiao WU; Lei ZUO; Wanjie SONG; Huimin LIU

doi:10.11999/JEIT180024

Volume 40 Issue 11

Oct. 2018

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Ming LI, Jiaojiao WU, Lei ZUO, Wanjie SONG, Huimin LIU. Aircraft Target Classification and Recognition Algorithm Based on Measured Data[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2606-2613. doi: 10.11999/JEIT180024

Citation:

Ming LI, Jiaojiao WU, Lei ZUO, Wanjie SONG, Huimin LIU. Aircraft Target Classification and Recognition Algorithm Based on Measured Data[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2606-2613. doi: 10.11999/JEIT180024

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Aircraft Target Classification and Recognition Algorithm Based on Measured Data

doi: 10.11999/JEIT180024

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
Collaborative Innovation Center of Radar at Xidian University, Xi’an 710071, China

Funds: The National Defense Foundation of China (61424010302162401002), The National Natural Science Foundation of China (61501342), The Shaanxi Natural Science Foundation of China (2017JM6019)

Received Date: 2018-01-08
Rev Recd Date: 2018-06-21

Available Online: 2018-07-20

Publish Date: 2018-11-01

Abstract

Abstract

After analyzing the features of three measured data from the low-resolution radar system, corresponding to the helicopter, the propeller, and the turbojet, an algorithm is proposed by using multiple features to classify and recognize the aircraft targets. First, multiple features are extracted, including Doppler frequency shift, relative magnitude, waveform entropy of time and frequency domain, and time-frequency domain features from the measured data. Then, these features are utilized for classification purpose by means of the Support Vector Machine (SVM). Finally, owing to the symmetry and the width of time-frequency distributions of the returned signals between the helicopters with odd and even blades, a method is proposed to recognize of helicopter. The experimental results of measured data verify the effectivity of the proposed algorithms.
- Target classification,
- Feature extraction,
- Time-frequency signatures,
- Helicopter recognition

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References(14)

References

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