# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 1 Introduction  
Underwater target tracking technology is widely used in marine resource exploration, underwater engineering operation, sea battlefield monitoring, and precise underwater weapon homing and it has significant prospects for military-civilian integration [1, 2, 3, 4, 5, 6, 7]. It can locate the azimuth and distance of a target by detecting the target's acoustic signals (such as shell vibration frequency, propeller rotation noise, etc.) to determine the tracking of the target [8]. Recently, researchers have also made some advancements in the field of underwater target tracking technology. [PERSON] [9] proposed an online processing framework that was based on forward looking sonar (FLS) images and presented a novel tracking method that was based on the use of a Gaussian particle filter (GFP) to solve the problem of continuous multi-target tracking in clutter. [PERSON] [10] studied a variational Bayesian particle filter, which relies on a Monte Carlo method of approximating the posterior distribution of a moving underwater target's location using a weighted set of sample points (particles) that evolve in time with the target. [PERSON] [11] studied the use of automatic threshold detection and parabolic beam interpolation to obtain accurate target azimuths and proposed a preset tracking method to realize the continuous detection and tracking of the target azimuth. [PERSON] [12] proposed an unscented particle filter approach to estimate the accuracy of the target motion parameters (TMPs). The Kalman filtering method is also widely used in the field of underwater target tracking. [PERSON] [13] proposed a new estimator, called the hybrid unscented Kalman filter, which combines three existing methods (UKF, the integration technique, and the pre-processing mechanism) to yield a much better performance than any of those methods individually. [PERSON] [14] used an extended color Kalman filter to estimate the mathematical model and attitude of transmission line inspection UAVs, ensuring the stable, reliable, and safe flight of the UAVs under electromagnetic interference. However, the above methods have some limitations. The Kalman filtering method depends on more accurate prior knowledge and various unknown disturbances in the actual sonar system may lead to serious estimation deviations. In addition, with the progress of artificial intelligence, deep learning has become one of the primary concerns of the target tracking field [15; 16; 17; 18; 19; 20; 21]. In recent years, scholars at home and abroad have researched target tracking based on deep learning and have proposed some new models. [PERSON] [22] proposed an end-to-end training full convolutional twin network, which regards single target tracking as a similarity learning problem, learns the embedding function in the off-line stage, uses dense and efficient sliding window evaluations, and calculates the cross-correlation between the two inputs in the bilinear layer. This method significantly reduces the amount of calculation, but it is only suitable for single target tracking. [PERSON] [23] proposed a dual-branch target tracking method based on a twin network, which solves the problem of the poor robustness of target tracking methods in complex scenes, such as target appearance changes. At present, the traditional neural network has been the focus of less research in the field of underwater target tracking because it needs a large number of datasets to be trained; however, for the problem of ocean target tracking, especially in the case of non-cooperative objectives, the marine database is seriously lacking and does not include enough training data, which leads to the insufficient training of neural networks and the instability of the model structure, resulting in the problem of significant tracking errors. In view of the above problems, we propose an LSTM-Kalman filtering method. In this method, we use an LSTM network to further modify the state update of the Kalman filtering process.