Image feature-based aircraft wake identification with coherent Doppler wind lidar

Aircraft wake vortices present substantial risks to aviation safety and significantly constrain airport operational efficiency. To address this, we propose a lightweight wake vortex identification method that exploits the hue, saturation, and value (HSV) color space combined with image texture features. This method relies neither on pre-collected datasets nor on preset parameters, and enables accurate and rapid localization of the wake vortex core region. By transforming lidar wind field data into the HSV color space, the algorithm leverages the high sensitivity of hue and saturation to velocity gradients, thereby enhancing the extraction of vortex core contours. Validation against numerical simulations yields root mean square errors (RMSE) of 2.57 m and 6.08 m for the left and right vortex core positions, respectively, with a circulation retrieval RMSE of 38.14 m²/s (10.37%). Field experiments conducted at Guangzhou Baiyun International Airport confirm the method's capability to accurately capture the evolution of high-altitude wakes and complex low-altitude ground effects. Crucially, the proposed approach significantly reduces computational complexity while maintaining high identification accuracy, offering a viable solution for real-time airport wake vortex monitoring systems.