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- Title
基于自注意力机制和CNN-LSTM的空战目标机动轨迹预测.
- Authors
李战武; 张帅; 乔英峰; 王强; 姜勇; 张飞
- Abstract
An air combat target maneuver trajectory is a multi-dimensional time series with rich temporal and spatial characteristics, and high degrees of complexity and uncertainty. At present, establishing kinematic models for trajectory prediction is very difficult. Besides, it is also difficult for time-series prediction methods to extract temporal and spatial features, and only single sequential training can be achieved from T to T+1. In this view, this paper proposes model CNN-LSTM-ATT, which combines Self-Attention(ATT) mechanism with Convolutional Neural Network(CNN) and Long Short-Term Memory(LSTM). This model is trained offline, and the obtained optimal model can realize high-precision prediction of target maneuver trajectories. Compared with CNN-LSTM and LSTM models for single-step prediction, this model has a good single-step prediction performance and different overload maneuvering prediction abilities. Considering the errors and missing of the transmitted data caused by electromagnetic interference and complex environment, the 5-step target trajectory prediction is carried out, and the prediction results and evaluation indexes are better than those of CNN-LSTM and LSTM models.
- Publication
Journal of Ordnance Equipment Engineering, 2023, Vol 44, Issue 7, p209
- ISSN
2096-2304
- Publication type
Article
- DOI
10.11809/bqzbgcxb2023.07.028