Projectile Impact Point Prediction Based on CNN-GRU Network under Small Samples
In order to fully explore the law of projectile radial velocity in time and space,and im-prove the accuracy of projectile impact-point prediction,a method of projectile impact point prediction based on CNN-GRU is proposed.CNN and GRU networks are used respectively to extract the strong correlation characteristics of the projectile radial velocity in time and space,to learn the highly complex nonlinear flight trajectory,and to build the prediction model of projectile impact points.The radial ve-locity data of a certain type of projectile is used as the training set and test set to predict the impact points,and compared with the time series prediction methods of MLP,LSTM and CNN-LSTM.The ex-perimental results show that the CNN-GRU prediction model can effectively extract the spatiotemporal information in the projectile radial velocity sequence,and learn the position of the projectile relative to the radar.The comparisons with other models show that the predication model has higher prediction ac-curacy,faster convergence speed and better stability.
radial velocityimpact-point predictionconvolutional neural networkgated recurrent unit
国家科技期刊平台
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