Random Forest Aircraft Wake Recognition Based on Feature Fusion

To improve the efficiency and safety of civil aviation transportation, the accurate recognition of aircraft wake vortex should be realized. We use a random forest model, combining with Doppler lidar technology, and propose a feature fusion method based on radial velocity range and edge contour. Experiment extracts the speed range characteristics of the data samples collected at Shuangliu Airport, and at the same time maps the sample data into a grayscale image, and extracts the image contour features through morphological gradients to construct a random forest wake recognition model, which is the same as a single. The characteristic method and the decision tree are compared and tested. The experimental results show that the classification accuracy, precision, recall, and F1-score of the random forest model after feature fusion are 95.8%, 87.3%, 89.4%, and 88.4%, respectively, which are higher than the recognition results of single feature method and decision tree model. The established method can detect aircraft wake vortex in wind fields with complex backgrounds.