1. Air combat target maneuver trajectory prediction based on robust regularized Volterra series and adaptive ensemble online transfer learning
- Author
-
Xi Zhi-fei, Kou Ying-xin, Li Zhan-wu, Lv Yue, Xu An, Li You, and Li Shuang-qing
- Subjects
Maneuver trajectory prediction ,Volterra series ,Transfer learning ,Online learning ,Ensemble learning ,Robust regularization ,Military Science - Abstract
Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and maneuver decision-making. However, 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 solved. To 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 proposed. Firstly, 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 data. Secondly, 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 Tr-Volterra 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 error. Finally, 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 models. Compared 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 prediction. The rationality and effectiveness of the proposed algorithm are verified by simulation on three sets of chaotic time series data sets and a set of real target maneuver trajectory data sets.
- Published
- 2023
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