动态扩展蚁巢模型的通航报告位置预测算法  

General Aviation Report Location Prediction Based on Dynamic Extended Ant Nest Model

作　　者：王鹏[1] 张喆[1] 王晓亮[1] 吴仁彪[1] Wang Peng;Zhang Zhe;Wang Xiaoliang;Wu Renbiao(Tianjin Key Lab for Advanced Signal Processing,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学天津市智能信号与图像处理重点实验室

出　　处：《信号处理》2019年第12期1942-1951,共10页Journal of Signal Processing

基　　金：国家自然科学基金委员会与中国民航局联合资助项目(U1633107);中央高校基本科研业务费项目中国民航大学专项(3122018D010)

摘　　要：为满足我国通用航空器动态预测的迫切需求,提出了动态扩展蚁巢模型的通航报告位置预测算法。该算法根据错误报告点预测信息动态扩展新蚁巢,根据蚁巢间的相互排斥性将扩展的新蚁巢与临近蚁巢间的路径点添加到路径禁忌区,从而减少路径点选择范围,降低迭代次数及收敛于局部最优解的概率,在提高算法正确率的同时兼顾搜索效率。仿真实验表明该算法能够满足通用航空器动态预测的业务需求,为保障通用航空器安全飞行提供技术参考。In order to meet the urgent need of dynamic prediction for general aircraft,a general aviation report location prediction algorithm based on dynamic extended ant nest model was proposed.The algorithm expanded the new ant nest dynamically according to the prediction information of error reporting points.According to the mutual exclusion between nests,the path points between extended new nests and adjacent nests were added to the path taboo area.Thus,the selection range of path points was reduced,the number of iterations and the probability of convergence to the local optimal solution were reduced,and the search efficiency was taken into account while improving the accuracy of the algorithm.The simulation results show that the algorithm can meet the business requirements of general aircraft dynamic prediction and provide technical reference for tracking flight dynamics and ensuring flight safety of general aircraft.

关 键 词：通用航空 报告点预测 蚁群算法 扩展蚁巢 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]