| Formation cluster has become the mainstream mode of battlefield.The combat objects generally carry out scheduled plans in a cluster mode,in which the specific communication data link for information transmission is applied.It is essential to recognize the intention of formation cluster correctly for making decisions and striking the objects accurately.This study analyzes the cluster intention from the perspective of communication event characteristics and external monitoring status of the formation cluster.Based on the characteristics of combat group cooperation and communication link configuration in a country,the behavior data simulation is carried out.In addition,the intention recognition model is established and its validity is verified on the simulation data set.The main contents are as follows:1.A definition of the cluster communication behavior under the assumed fixed cluster intention is given.Aiming at the intention recognition of enemy cluster in complex battlefield environment,this research focuses on combining the characteristics of communication events with external monitoring status(speed,height,location et.,)and analyzing the behaviors of combat objects,communication individuals,communication data links and cluster states under different intentions.The cluster behavior intention simulation system is established,and the behavior data of the cluster under different intentions is generated to form a behavior event library.2.A hyper-parameter optimization method based on Tree-structured Parzen Estimators(TPE)is proposed.Aiming at the selection of a large number of hyper-parameters in deep learning networks,the TPE hyper-parameter optimization method based on Sequential Model-Based Optimization(SMBO)process is adopted to optimize the model.Experimental results show that the TPE algorithm has a better performance in hyper-parameter optimization,since it can effectively address the dimension catastrophe of grid search and the problem that random search may miss the optimal combination point.3.An intention recognition method using recurrent neural network which is based on Attention mechanism is proposed.By considering the recurrent neural network as the main research method and combining the idea of Attention mechanism,a recurrent neural network-Attention intention recognition method is presented.The simulation data is feeding into the recurrent neural network for data calculation.The Attention simulates the attention process of human beings.By focusing on the output of the recurrent neural network,thedifferentiation weight is distributed,leading to the improvement of the accuracy of intention recognition.4.The cluster intention recognition experiment is carried out.The hyper-parameters of the model are optimized by experiments,and the results of the hyper-parameters optimization are configured into the recognition model.The accuracy of the simulation data on different models is tested,and the performance of the model is evaluated by multi-index evaluation method.The simulation results show that the hyper-parameter optimization method based on TPE algorithm calculate the optimal hyper-parameter combination point in the search space more stably.The recurrent neural network based on Attention mechanism can notice the influence of different features on the intention result,and the recognition result is more accurate. |
