# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 6 Conclusions  
In this paper, we developed a method for underwater target tracking, which yielded consistently satisfactory results. Firstly, the target echo signal was azimuth weighted using beamforming to enhance the target signal. Then, the beam domain signal was subjected to a short-time Fourier transform to obtain the time-frequency spectrum and the target was detected by the CNN model. Finally, the long-term and short-term memory artificial neural network was modified to the Kalman filtering method to obtain the optimal estimation results. According to the characteristics of the underwater targets, we constructed corresponding datasets and trained the CNN and LSTM-NN models. The test results showed that the model had a high accuracy and could be used for underwater target tracking. Then, we simulated the proposed LSTM-Kalman filtering method and verified it using an actual sea trial. The results showed that this method effectively solved the problem of large deviations in underwater target tracking. It also solved the shortcomings of the traditional Kalman filtering method, which is only suitable for linear systems and requires accurate prior knowledge, and the proposed method also improved the accuracy of the tracking results and the robustness of the system.  
On the other hand, considering that targets may become lost or incorrect in the tracking process, a target detection model was added to judge the tracking value at a particular time. When a target appeared for the first time and the confidence score that was output by the target detection model was greater than or equal to 0.85, it indicated that the target was of tracking value and the tracking model started tracking. When a target appeared in the tracking process and the target feature that was detected at the current time was consistent with the target feature of the previous time point, the tracker continued to work, otherwise it started tracking a new target. The method proposed in this paper reduced the amount of calculation required for the system, improved the accuracy of tracking, and significantly reduced the tracking deviations, which has not been achieved using other methods.  
The simulation and actual sea trial data indicate that this method is suitable for linear and nonlinear systems, the target position tracking error is significantly reduced compared to the traditional method, and it delivers a stable and fast tracking performance. An innovation of this paper was that an LSTM-NN model was adopted, which made up for the defect of the Kalman filtering method only being suitable for linear systems, reduced the target tracking deviations, and improved the robustness of the system, all of which are important to the practical applications of underwater target tracking. Another innovation was that the target detection model could judge the tracking value of the target and prevent the target from becoming lost or incorrect in the tracking process, which is not available in other methods. The LSTM-Kalman filtering method produced a better tracking performance than the traditional Kalman filtering method and has practical significance for subsequent marine exploration and the establishment of a marine database.  
**Author Contributions:** Conceptualization, C.X. and M.W.; Methodology, C.X. and C.Z.; software, C.X.; validation, C.X., Y.G. and C.Z.; formal analysis, C.Z.; investigation, B.Q.; resources, M.W. and C.X.; data curation, B.Q.; writing--original draft preparation, C.X.; writing--review and editing, C.X., M.W. and C.Z.; visualization, C.X.; supervision, M.W.; project administration, M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.