联合战术信息分发系统(Joint Tactical Information Distribution System,JTIDS)作为Link16的通信载体为多军种协同作战提供服务。当前,高精尖武器装备的数量激增,进而JTIDS终端增多,导致非正交跳频层叠网间的邻频干扰和同频干扰发生的...联合战术信息分发系统(Joint Tactical Information Distribution System,JTIDS)作为Link16的通信载体为多军种协同作战提供服务。当前,高精尖武器装备的数量激增,进而JTIDS终端增多,导致非正交跳频层叠网间的邻频干扰和同频干扰发生的概率很大。基于此,分析了改用高斯最小频移键控(Gaussian Minimum Shift Keying,GMSK)调制后,JTIDS的频谱特征。同时,仿真分析了改进系统对于互干扰和同频干扰的健壮程度。仿真结果表明,在邻频干扰和同频干扰下,JTIDS误码率显著提高,使用复杂度更高的GMSK解调的JTIDS性能比原系统更好。展开更多
The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms...The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.展开更多
文摘联合战术信息分发系统(Joint Tactical Information Distribution System,JTIDS)作为Link16的通信载体为多军种协同作战提供服务。当前,高精尖武器装备的数量激增,进而JTIDS终端增多,导致非正交跳频层叠网间的邻频干扰和同频干扰发生的概率很大。基于此,分析了改用高斯最小频移键控(Gaussian Minimum Shift Keying,GMSK)调制后,JTIDS的频谱特征。同时,仿真分析了改进系统对于互干扰和同频干扰的健壮程度。仿真结果表明,在邻频干扰和同频干扰下,JTIDS误码率显著提高,使用复杂度更高的GMSK解调的JTIDS性能比原系统更好。
文摘The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.
