# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 5 Discussion  
Due to the lack of training data, datasets of target signals are not easy to construct and the batch training of labeled data cannot be realized. Therefore, the application of deep learning in the field of target tracking is not widely used. In addition, the traditional Kalman filtering method has high rates of system errors and measurement errors at the last time point. When there is significant interference in the actual situation, it causes large errors in the final estimation results and even the divergence of the filtering results. This impacts target tracking performance, so we proposed a new target tracking method. Firstly, a CNN learned the time-frequency characteristics of a target echo signal, which was conducive to the detection of the subsequent echo signal. Then, an LSTM-NN modified the traditional Kalman filtering method to obtain the optimal estimation result and reduce the impact of accidental errors, such as system errors and observation errors.  
We verified the new method using simulation and actual sea experiments. In the simulation experiments, it was found that the closer the trajectory of the moving target to linearity, the more accurate the Kalman filtering method. This also confirmed that a prerequisite of the traditional Kalman filtering method is to assume that the state space model is a linear system. In addition, we used an LSTM network to modify the traditional method, which not only increased the unique time information of the sequence signals and retained the characteristics of the target echo signals that were associated with the time point, but also used the intermediate variables of the traditional Kalman filter for training and learning to extract the core idea of the Kalman filter. Therefore, we combined the two to capitalize on their advantages and ensure their operational efficiency under the conditions of improving the accuracy of the estimation results.  
This method avoids the defect of the Kalman filter needing prior knowledge and solves the problem of insufficient training data for target tracking. The LSTM-Kalman filtering method combines the advantages of the two models, uses the framework of the  
Figure 16: Comparison diagram of the target tracking deviations between the Kalman filtering method and the LSTM–Kalman filtering method.  
Kalman filter to predict the target motion state in one step, and then uses a neural network to improve the ability of nonlinear relationships. The correction of the traditional Kalman filtering method can be further adjusted to eliminate the estimation deviations that are caused by the inaccuracies of \(\mathbf{Q}\) and \(\mathbf{R}\) in practical applications.  
Under the experimental conditions described in Section 3, the average error of the Kalman filtering method that was modified using the LSTM network was reduced by 60% compared to the traditional Kalman filter, indicating that its tracking effect was significantly improved. In the actual sea trial described in Section 4, the target tracking environment was more complex and there were many uncertain factors, but the test results were still relatively ideal. Compared to the nonlinear Kalman filtering method, the LSTM-Kalman filtering method reduced the tracking error significantly, showing that the method is suitable for actual marine environments.