非完备信息下无人机空战目标意图预测

Prediction of unmanned aerial vehicle target intention under incomplete information

实际空战的复杂性和不确定性及部分空战信息未知性,给无人机空战目标意图预测带来巨大挑战.针对非完备信息下无人机空战目标意图预测问题,本文提出了一种基于长短时记忆(long shortterm memory,LSTM)网络的非完备信息下空战目标意图预测模型.采用分层的方法建立空战目标意图预测特征集,并将空战信息编码成时序特征,将专家经验封装成标签,引入三次样条插值函数拟合以及平均值填充法来修补不完备数据,利用自适应矩估计(adaptive moment estimation,Adam)优化算法,加快目标意图预测模型训练速度,以便有效地防止局部最优的问题.最后通过仿真验证了所建立的无人机空战目标意图预测模型能有效预测无人机空战目标意图.

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.