Prediction of unmanned aerial vehicle target intention under in- complete information

Abstract

The complexity and uncertainty of actual air combat and the unknown information of some air combat bring great challenges to unmanned aerial vehicle (UAV) air combat target intention prediction. In this paper, we examine the problem of air combat intention prediction under incomplete information, and present an air combat target intention prediction model based on long-short-term memory (LSTM) with incomplete information. The model adopts a hierarchical method to establish the feature set of air combat target intention prediction, encodes the information of air combat to time series features, encapsulates expert knowledge into labels, and introduces the method of fitting cubic sample interpolation function and filling average value to repair incomplete data. Also, we used the adaptive moment estimation (Adam) optimization algorithm to accelerate the training speed of the model to effectively prevent local optimum. Finally, the simulation results show that the proposed model can effectively predict the target intention of UAVs in air combat.