Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAīall Aerospace & Technologies Corp.Chicago’s homeless population faces challenges year-round, but the conditions of their lives are brought into stark relief during extreme weather, such as the polar vortex that descended upon the region in January 2019. NASA Goddard Space Flight Center, Greenbelt, MD, USA Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition.

In: Intelligent Computing: Image Processing Based Applications, pp. Sultana, F., Sufian, A., Dutta, P.: A review of object detection models based on convolutional neural network. Liu, X., Zhang, X., Zhai, X., Zhang, H., Liu, B., Wu, S.: Observation of aircraft wake vortex evolution under crosswind conditions by pulsed coherent Doppler lidar. Köpp, F., Rahm, S., Smalikho, I.: Characterization of aircraft wake vortices by 2-μm pulsed Doppler lidar. Gerz, T., Holzäpfel, F., Bryant, W., Köpp, F., Frech, M., Tafferner, A., Winckelmans, G.: Research towards a wake-vortex advisory system for optimal aircraft spacing. Wu, S., Zhai, X., Liu, B.: Aircraft wake vortex and turbulence measurement under near-ground effect using coherent Doppler lidar. This method is expected to be applied to the development of a real-time wake vortex monitoring system and further compress the existing wake vortex spacing criteria, which has broad application prospects in the field of airport aviation security assurance.

The results show that deep learning models can significantly improve the recognition and classification efficiency of aircraft wake vortices. The speed and accuracy of recognition and classification could be improved significantly especially when atmospheric turbulence or other interferences exist. In this method, a large amount of wake vortices generated by multiple aircrafts in different weather conditions are collected to build the radial velocity and spectrum width (RV-SW) data sets and to be trained using the CNN models, including the target recognition model and the image classification model. This chapter focuses on the development of a real-time wake vortex monitoring system which will significantly improve the detection efficiency of the wake vortex and the introduction of a quick image recognition method to identify the wake vortex based on convolutional neural network (CNN) models. However, the post-processing algorithm cannot meet the needs of real-time detection of wake vortices at airports due to the long-time consumption. The pulsed coherent Doppler lidar (PCDL) has been widely used in the observation of aircraft wake vortex due to its advantages of high spatial-temporal resolution and high precision. Aircraft wake vortices are serious threats to aviation safety and limit airport capacity greatly.

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