光电工程, 2019, 46 (7): 190082, 网络出版: 2019-07-25
基于 AlexNet卷积神经网络的激光雷达飞机尾涡识别研究
Research on aircraft wake vortex recognition using AlexNet
尾涡识别 AlexNet卷积神经网络 目标识别 多普勒激光雷达 wake vortex identification AlexNet convolution neural network target recognition Doppler LiDAR
摘要
为解决飞机尾涡威胁后机飞行安全问题,保障空中交通安全,提高机场和空域容量,提出了一种基于 AlexNet卷积神经网络模型的算法,实现飞机尾涡的准确识别。结合多普勒激光雷达探测原理和 Hallck-Burnham尾涡速度经典模型,构建了 AlexNet神经网络模型提取大气风场中的尾涡速度云图的图像特征,识别飞机尾涡。研究表明,该模型能够准确识别目标空域中的飞机尾涡,网络模型收敛后对尾涡识别的准确率高达 91.30%,并具有低虚警率,能有效地实现对飞机尾涡的识别和预警,达到尾涡监测的目的。
Abstract
In order to solve the flight safety issues threatened by wake vortex of leading aircraft, ensure air traffic safety, and improve the capacity of airdrome and airspace, an AlexNet convolutional neural network model algorithm is proposed to identify aircraft wake vortex. Combined with the detection principle of Doppler LiDAR and the classic model of Hallck-Burnham wake vortex velocity, the AlexNet neural network model was constructed to extract the image features of the wake vortex velocity images in the atmosphere and identify the aircraft wake vortex. The re-search shows that the model is able to accurately identify the aircraft wake vortex in the target airspace. After the network model converges, the accuracy rate reaches to 91.30%, which can effectively realize the identification work. Meanwhile, this study also demonstrates the low probability of false alarm of the AlexNet neural network in detecting wake vortex, which meets the requirement of early warning and monitoring of the aircraft wake vortex.
潘卫军, 段英捷, 张强, 吴郑源, 刘皓晨. 基于 AlexNet卷积神经网络的激光雷达飞机尾涡识别研究[J]. 光电工程, 2019, 46(7): 190082. Pan Weijun, Duan Yingjie, Zhang Qiang, Wu Zhengyuan, Liu Haochen. Research on aircraft wake vortex recognition using AlexNet[J]. Opto-Electronic Engineering, 2019, 46(7): 190082.