An interpretable real-time maneuver identification algorithm based on early time series classification
PANG Nuo-yan，GUAN Dong-hai，YUAN Wei-wei
The maneuver identification of fighter aircraft is the basis for judging their tactical intentions, but the existing maneuver identification methods have weak real-time performance and lack interpretability, which cannot meet the real-time requirements in air combat and are not conducive to human-machine trust. This paper designs a real-time maneuver identification algorithm based on early time- series classification, which divides the complete maneuver into maneuver units and uses ensemble learning algorithm to recognize and monitor the maneuver units in real-time, in order to achieve real-time requirements and obtain high recognition accuracy. The algorithm uses interpretable models and explains the model through feature contribution, making the model more transparent and reducing the decision risk for air combat decision-makers. Nine different maneuvers, such as hovering and jackknifing, are selected for simulation experiments, which proves that the algorithm can complete the identification with only the first 20% of the sample data of the time series observed, and the identification accuracy can reach 93%.
PANG Nuo-yan, GUAN Dong-hai, YUAN Wei-wei. An interpretable real-time maneuver identification algorithm based on early time series classification[J]. Computer Engineering & Science, 2024, 46(02): 353-362.
