The invention discloses a marine scene unmanned aerial vehicle-to-ship line-of-sight path probability prediction method, which comprises the following steps: comprehensively considering the diversity of a marine environment, further subdividing the marine scene into 12 representative marine sub-scenes, and optimizing and improving an LoS probability expression; training to obtain corresponding Dm, Dd and Db parameter values in each group of scenes by using a two-channel convolutional neural network and spider monkey optimization coupling machine learning method; the LoS probabilities in different sub-scenes are calculated by inputting the distance between the unmanned aerial vehicle and the ship. The method comprehensively considers the marine environment characteristics and the motion of the unmanned aerial vehicle and the ship, considers the influence of the flight height of the unmanned aerial vehicle on signal transmission, and improves the prediction accuracy.
本发明公开了一种海洋场景无人机对船视距路径概率预测方法,包括:综合考虑海洋环境的多样性,将海洋场景进一步细分为12种代表性海洋子场景,对LoS概率表达式进行了优化改进;利用双通道卷积神经网络与蜘蛛猴优化耦合的机器学习方法训练得到每一组场景下对应的Dm、Dd和Db参数值;通过输入无人机和船舶之间的距离,计算不同子场景下的LoS概率。本发明综合考虑海洋环境特征、无人机和船舶的运动,并考虑无人机飞行高度对信号传输的影响,提高了预测的精确度。
Marine scene unmanned aerial vehicle-to-ship sight distance path probability prediction method
一种海洋场景无人机对船视距路径概率预测方法
2024-12-10
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |