# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
## 1 Introduction  
In marine activities, active sonar is widely used in underwater moving target detection, recognition, tracking, seabed scanning, navigation, and communication [1, 2, 3, 4, 5, 6, 7]. Active sonar is an important device for active tracking of underwater acoustic targets. By periodically transmitting specific signals and analyzing the echo signals received by the array, the target's position, speed, distance, and other characteristic information can be obtained [8]. In recent years, due to the strong demand for marine development research and marine safety, higher requirements have been put forward for the application of active sonar in tracking [9]. Therefore, the research on the active tracking method of underwater moving target has important engineering significance and practical significance. The current active tracking method for underwater acoustic targets [10] has the following two drawbacks: when the azimuths of two different moving targets overlap, the target data cannot be correctly correlated, leading to the loss of one target; and when the target echo signal is weak and below the detection threshold set by the target verdict, it is more difficult to track such weak SNR targets. In the active tracking of underwater acoustic targets, accurately detecting targets with weak SNR from the signal and correctly correlating the data of different targets has become a hot issue of research. The Kalman filter active tracking (KFAT) method combined with Kalman filter is the most commonly usedmethod for active tracking of hydroacoustic targets, which transforms the azimuth and distance information of the target from polar coordinates to Cartesian coordinates, and then establishes a motion model of target for processing [11], which can achieve the prediction of azimuth and distance of the tracked target and denoise the tracking results to improve the tracking accuracy [12]. [PERSON] [13] proposed that when tracking a moving target, in the case of decorrelation processing on the conversion deviation of position and distance, the linear part (position measurement) and the nonlinear part (distance-rate measurement) are respectively passed through Kalman filtering and unscented Kalman filter (UKF) for processing. [PERSON] [14] studied the application of probabilistic data association (PDA) technology in different target tracking schemes, especially targets with low signal-to-noise ratio (SNR). The accuracy of the target azimuth and distance obtained after filtering depends on the original data measurement, which reduces the deviation of the initial measurement and can improve the accuracy of active tracking. The information on target scattering characteristics carried in the active sonar echo signal can be used to detect and distinguish different targets [15]. Researchers have used target scattering characteristics for target detection or classification in the field of underwater acoustics [16; 17]. In 2007, [PERSON] [18] proposed a method to extract features from the target echo signal by imitating auditory perception, and classify the target using Gaussian classification in machine learning. Deep learning has the advantage of automatically extracting target feature information from raw data through learning training and can be used for multi-target recognition and classification. The convolutional neural network (CNN) is a commonly used structure in deep learning that has greatly improved productivity and efficiency in areas such as computer vision, natural language processing, text and speech recognition, and object detection [19]. In 2015, [PERSON] [20] used CNN and existing auditory perception models to extract features of target radiated noise using mel-scale frequency cepstral coefficients to simulate the function of a complete auditory system to identify ship target radiated noise. The results show the feasibility of applying deep learning to the field of underwater acoustics.