Deep learning for aircraft classification from VHF radar signatures
Jérémy Fix, Chengfang Ren, Arthur Costa Lopes, Guillaume Morice, Shuwa Kobayashi, Thierry Leterte, Israël Hinostroza
Abstract
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‐broadcast data).