Aircraft wake recognition based on deep learning

Fulin Zhang; Jianbing Li; Hang Gao

doi:10.1117/12.2627022

27 March 2022 Aircraft wake recognition based on deep learning

Fulin Zhang, Jianbing Li, Hang Gao

Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 12169BQ (2022) https://doi.org/10.1117/12.2627022
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

Aircraft wake is a pair of strong counter rotating vortices formed behind the aircraft in flight. Its rapid identification is the basis of wake characteristic parameter inversion, which has important application requirements in the field of aviation safety. In this paper, convolution neural network based on improved alexnet structure is used to deeply learn the characteristics of aircraft wake. Firstly, the alexnet network model is improved based on the aircraft wake characteristics and recognition requirements, and then the model is trained based on a large number of lidar wake observation data. The results show that the accuracy of the model in the test set reaches 97%, and it has good generalization ability, and can quickly and accurately identify the aircraft wake under complex background wind field.

Citation Download Citation

Fulin Zhang, Jianbing Li, and Hang Gao "Aircraft wake recognition based on deep learning", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 12169BQ (27 March 2022); https://doi.org/10.1117/12.2627022

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