首页|基于AM-LSTM的飞行区航空器滑行轨迹预测与冲突识别

基于AM-LSTM的飞行区航空器滑行轨迹预测与冲突识别

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为解决航空器点源定位难以有效预测而引发冲突风险愈来愈多的问题,构建基于注意力机制(AM)和长短期记忆网络(LSTM)的时间序列轨迹预测模型AM-LSTM,预测未来短时间内飞行区航空器的瞬时点源位置;在此基础上,根据航空器型号和滑行航向对其进行轮廓扩展,以航空器速度作为安全距离权重,通过射线法实现轮廓冲突的判定;并以乌鲁木齐地窝堡机场为例进行验证,利用训练完成的轨迹预测模型预测飞行区航空器滑行轨迹,以识别航空器轮廓间的滑行冲突.结果表明:AM-LSTM预测模型能够准确预测飞行区航空器运动轨迹.未来3s内轨迹位置预测的平均位移误差为1.05 m,轨迹点位置预测精准性可达 94.37%,故能在轨迹预测的基础上精确识别滑行冲突风险,有利于保障飞行区的安全运行.
Aircraft taxiing trajectory prediction and conflict risk identification in airfield area based on AM-LSTM
In order to address the increasing risk of conflict caused by the difficulty in effectively predicting aircraft point source localization,a time series trajectory prediction model AM-LSTM based on AM and LSTM was constructed,to predict the instantaneous point source location of the aircraft in the airfield area in a short time in the future.On this basis,the contour was expanded according to the aircraft type and glide heading,the aircraft speed was used as the safety distance weight,and the ray method was used to realize the determination of the contour conflict.Urumqi Dewopu Airport was used as an example for validation,and the trained trajectory prediction model was utilized to predict aircraft taxiing trajectories in the airfield area and identified taxiing conflicts between aircraft profiles.The results show that the AM-LSTM prediction model can accurately predict the aircraft movement trajectory in the airfield area,and the average displacement error of the trajectory position prediction in the next 3 s is 1.05 m,and the accuracy of trajectory point position prediction can reach 94.37%.Therefore,it can accurately identify the risk of taxiing conflict on the basis of trajectory prediction,which is conducive to guaranteeing the safe operation of the airfield area.

attention mechanism(AM)long short term memory(LSTM)airfield areaaircraft taxiingtaxiing trajectory

王兴隆、许晏丰

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中国民航大学 民航飞联网重点实验室,天津 300300

注意力机制(AM) 长短期记忆网络(LSTM) 飞行区 航空器滑行 滑行轨迹

国家自然科学基金国家自然科学基金中央高校基金重点项目天津市科技计划项目

U213320762173332312201919121JCYBJC00700

2024

中国安全科学学报
中国职业安全健康协会

中国安全科学学报

CSTPCD北大核心
影响因子:1.548
ISSN:1003-3033
年,卷(期):2024.34(1)
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