Non-cooperative target track prediction method based on Attention-LSTM

The traditional LSTM network cannot predict the track of non-cooperative targets, because the data collected from non-cooperative targets has partial data loss, and it cannot form equal interval sampling data. The cubic spline method can be used to interpolate the data at equal intervals, but the contribution of the interpolated points to track prediction is much lower than that of the measured points. Therefore, the contribution matrix of track prediction is constructed by the Attention mechanism, and the weight matrix of LSTM network training is used to determine track prediction together. The experimental results show that the average accuracy of the test set can reach 96.25%, and the loss function value is about 0.125. The prediction accuracy is improved by 14.8% compared to traditional LSTM networks, which verifies the practicability and effectiveness of this method in non-cooperative target track prediction.