现代电子技术2026，Vol.49Issue(18)：11-17,26,8.DOI:10.16652/j.issn.1004-373X.2026.18.003
基于卷积神经网络的进近阶段飞机着陆距离预测
CNN-based prediction of aircraft landing distance in the approach phase
摘要
Abstract
In order to prevent aircraft runway overruns during landing,a method of real-time landing distance prediction based on the fusion of convolutional neural network(CNN)and kinematic model is proposed.The landing characteristic parameters are extracted based on flight data during the aircraft approach phase,and the final approach speed is predicted by means of CNN model.The predicted speed is used as input to calculate the landing distance in conjunction with the kinematic model.Taking the Boeing 737-800 as an example,the predicted landing distance is compared with the actual landing distance and the influencing factors of the model are analyzed.The experimental results show that the prediction results are consistent with the actual situation,and the number of samples,time node to the runway threshold,and time window have significantly impact on the prediction accuracy.The larger the number of samples,the higher the prediction accuracy.When the sample size increases to 300,the prediction accuracy tends to stabilize.The closer the time node to the runway threshold,the higher the prediction accuracy.As the time window increases,the prediction accuracy shows a fluctuating trend,and the prediction accuracy reaches the maximum value when the time window is 10 s.This prediction method has high prediction accuracy and strong stability,with an average error of 4.54%,and can realize real-time prediction,which provides technical support for the research and development of the airborne landing distance early warning system.关键词
卷积神经网络/快速存取记录器(QAR)数据/进近阶段/着陆距离/动力学模型/飞行安全Key words
CNN/quick access recorder(QAR)data/approach phase/landing distance/kinematic model/flight safety分类
信息技术与安全科学引用本文复制引用
回忆,王玺翔,庄南剑,杨雪雅..基于卷积神经网络的进近阶段飞机着陆距离预测[J].现代电子技术,2026,49(18):11-17,26,8.基金项目
国家重点研发计划(2023YFB4302903) （2023YFB4302903）
民航航空公司人工智能重点实验室资助项目 （）
中央高校基本科研业务费项目(3122025066) （3122025066）
