An interpretable real-time maneuver identification algorithm based on early time series classification
The maneuver identification of fighter aircraft is the basis for judging their tactical inten-tions,but the existing maneuver identification methods have weak real-time performance and lack inter-pretability,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 learn-ing algorithm to recognize and monitor the maneuver units in real-time,in order to achieve real-time re-quirements 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 se-lected 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%.
early time series classificationmaneuver identificationinterpretableensemble learning
万方数据
维普
