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Deep learning for aircraft classification from VHF radar signatures

Abstract : Radio sources in the Very High Frequency (VHF) band can be seized as opportunity donors in a passive radar configuration such as FM radio stations and VHF omnidirectional range (VOR). A full-wave simulation of three size classes of aeroplanes shows that their bistatic radar cross-section (RCS) are statistically comparable, albeit perform differently in time while the plane is flying. This difference can be exploited to recognize the size of the aeroplanes with respect to these classes. Measurements confirm this possible differentiation between the aeroplanes within the same class. Encouraging initial results were obtained using convolutional or recurrent neural networks to classify aircraft classes, combining simulated bistatic RCS results and real trajectories (collected from automatic dependent surveillance-broadcastdata).
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https://hal.archives-ouvertes.fr/hal-03227950
Contributor : Fix Jeremy <>
Submitted on : Monday, May 17, 2021 - 4:47:32 PM
Last modification on : Thursday, May 27, 2021 - 10:34:44 AM

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rsn2.12067.pdf
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Jérémy Fix, Chengfang Ren, Arthur Costa Lopes, Guillaume Morice, Shuwa Kobayashi, et al.. Deep learning for aircraft classification from VHF radar signatures. IET Radar Sonar and Navigation, Institution of Engineering and Technology, 2021, ⟨10.1049/rsn2.12067⟩. ⟨hal-03227950⟩

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