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
For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is p...For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.展开更多
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.
基金Supported by the National Basic Research Program of China (973 Program) (2006CB303000)
文摘For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.
