| Summary: | To solve the problem of low track prediction accuracy caused by the limitations of deep learning and the cumulative 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 obtain the final trajectory prediction value. Experiment results proved that the algorithm can significantly improve the accuracy of short-term prediction of airborne target tracks.
|
|---|