Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning

作     者：Xi Zhi-fei Kou Ying-xin Li Zhan-wu Lv Yue Xu An Li You Li Shuang-qing Xi Zhi-fei;Kou Ying-xin;Li Zhan-wu;Lv Yue;Xu An;Li You;Li Shuang-qing

作者机构：Air Force Engineering UniversityNo.1 Baling RoadBaqiao DistrictXi'anShaanxi ProvinceChina 

出 版 物：《Defence Technology（防务技术）》 (Defence Technology)

年 卷 期：2023年第20卷第2期

页      面：187-206页

核心收录：

学科分类：11[军事学] 08[工学] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 1109[军事学-军事装备学] 

基　　金：the support of the Fundamental Research Funds for the Air Force Engineering University under Grant No.XZJK2019040 

主　　题：Maneuver trajectory prediction Volterra series Transfer learning Online learning Ensemble learning Robust regularization 

摘      要：Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver ***,how to use a large amount of trajectory data generated by air combat confrontation training to achieve real-time and accurate prediction of target maneuver trajectory is an urgent problem to be *** solve this problem,in this paper,a hybrid algorithm based on transfer learning,online learning,ensemble learning,regularization technology,target maneuvering segmentation point recognition algorithm,and Volterra series,abbreviated as AERTrOS-Volterra is ***,the model makes full use of a large number of trajectory sample data generated by air combat confrontation training,and constructs a Tr-Volterra algorithm framework suitable for air combat target maneuver trajectory prediction,which realizes the extraction of effective information from the historical trajectory ***,in order to improve the real-time online prediction accuracy and robustness of the prediction model in complex electromagnetic environments,on the basis of the TrVolterra algorithm framework,a robust regularized online Sequential Volterra prediction model is proposed by integrating online learning method,regularization technology and inverse weighting calculation method based on the priori ***,inspired by the preferable performance of models ensemble,ensemble learning scheme is also incorporated into our proposed algorithm,which adaptively updates the ensemble prediction model according to the performance of the model on real-time samples and the recognition results of target maneuvering segmentation points,including the adaptation of model weights;adaptation of parameters;and dynamic inclusion and removal of *** with many existing time series prediction methods,the newly proposed target maneuver trajectory prediction algorithm can fully mine the prior knowledge contained in the historical data to assist the current predi