Short-term prediction algorithm of air target track based on residual correction CNN-BiLSTM
To solve the problem of low track prediction accuracy caused by the limitations of deep learning and the cumula-tive error generated by recursive prediction strategies,a short-term prediction algorithm for air target tracks based on residual correction CNN-BiLSTM was proposed.Firstly,a convolution module was introduced to extract potentially associated spatial location features from the track data,and a bidirectional long and short time memory network was used to extract temporal features from the track data,achieving real-time one-step prediction and multi-step advance prediction of air targets.Then,the integrated moving average autoregression was introduced as a residual correction model to model the residual generated by real-time one-step prediction,and the residual value of the hybrid neural network model for multi-step advanced prediction is calculated.Finally,the output results of the hybrid neural network model and the residual correction model are fused to ob-tain the final trajectory prediction value.Experiment results proved that the algorithm can significantly improve the accuracy of short-term prediction of airborne target tracks.
万方数据
维普
