**1. Introduction**

A side scan sonar can rapidly obtain large-area seabed images, has been widely used in seabed investigation, and plays an important role in seabed target detection [1–4] and investigation as well as research of the seabed ecological environment [5,6] due to its low cost and simple installation. A side scan sonar is usually dragged by a towing line to get close to the bottom of the sea to obtain high-resolution seabed images. Although the depth of the side scan sonar can be obtained by using depth sensors, the height of the sonar from the seabed cannot be accurately obtained [7]. Inaccurate sonar heights will lead to inaccurate geocoding sonar images [8], confuse the water column information with the seabed information, and cause serious problems in applications of target recognition and segmentation [9–11], image interpretation [12,13], and seabed sediment classification [14–16]. The bottom tracking of side scan data can accurately obtain the sonar height from the seabed by finding the first echo that reaches the seabed. Meanwhile, real-time bottom tracking can quickly detect changes in sonar height and seabed terrain, and enhance the safety of sonar equipment and ship navigation.

Side scan sonars can be installed on the vessel or towed close to the seabed from the survey ship. These sonars acquire high-resolution images by emitting sound pulses and recording the backscatter strengths from the water column and seabed [17]. The depth of the side scan sonar can be determined by the depth sensor, whereas the sonar height cannot be easily determined [18]. The sound wave transmits through the water column, and then arrives at the seabed. Given that the backscatter strengths from the water column are much lower than those from the seabed, the backscatter strengths recorded near the bottom positions differ from those recorded in other positions, which makes bottom position tracking possible [7].

With best practices, i.e., the gains are logged in the recorded files (e.g., \*.jsf file for EdgeTech sonars) and the gains are kept track in the processing chain, all useable information, including the raw signal levels and gains, are available. Then the bottom can usually be easily determined with a very high signal-to-noise ratio (SNR), which makes the signal level of the bottom tens of dB larger than in the water column [17]. However, when the gains are lost, detecting the bottom over the seafloor becomes much harder. Moreover, as the development of the oceanographic survey, more researchers are stepping into the relative fields of sonar imaging. In many cases, if the researchers are not there to record enough useable data during the survey, valuable information will be lost and these researchers can only study the recorded side scan data with very little information. In addition, old side scanside scan data often need to be reprocessed to find new results or to be compared with the current study. Given that the recorded side scan data are used for seabed imaging, the depths and gains are usually not recorded in the data (e.g., eXtended Triton Format \*.xtf files). In these situations, when the original sound signal levels are unknown and the echoes have been compensated with unknown gains (e.g., time varied gains), the recorded side scan data only include the converted backscatter strength data in special fixed ranges. Thereby, the bottom tracking methods are necessary. Moreover, certain effect factors, including sonar self-noises, ambient noises, and other object disturbances, also introduce challenges in bottom tracking methods [19].

To process these types of side scan data, most bottom tracking works are completed by using the threshold method assisted by expensive commercial software, such as Chesapeake SonarWiz and EdgeTech Discover [19]. Given that the threshold is usually determined on the basis of the operator's experience, this method also requires extensive manual work. Moreover, given the complexity of the seabed environment, the threshold changes during the processing. Using inappropriate threshold parameters can lead to incorrect bottom tracking results. Accordingly, researchers are looking for automatic algorithms to achieve enhanced efficiency. Some researchers have used the filtering method to remove noise, studied the variation features of the backscatter strengths of the side scan sonar, and used these feature differences for bottom tracking of the side scan data [20]. Given the continuity of sonar heights and the symmetry of the port and starboard side scan data, other researchers have built general models and used dynamic data filtering algorithms, such as Kalman filtering and time series, to repair abnormal data and improve accuracy [19]. Given the existence of many types of effect factors, the variations in backscatter strengths typically show a feature of regularity and local randomness. Traditional methods require manual threshold parameters or time-consuming post-processes, which, thereby, cannot guarantee accurate and real-time bottom tracking results.

Deep learning algorithms have been widely applied in image recognition and classification [21–24]. The one-dimensional convolutional neural network (1D-CNN) is a deep learning algorithm for processing one-dimensional sequence data, and has been proven to be an effective recognition and

classification method for one-dimensional sequence data [25,26]. After introducing the deep learning idea, algorithms can simulate the human brain, learn the variation feature of the local backscatter strength sequence, and fulfill the bottom tracking of side scan sonars. Therefore, on the basis of the recognition of side scan bottom data sequences through 1D-CNN, this paper presents a new real-time bottom tracking method for side scan sonar data. First, the operation theory of the side scan sonar and the characteristics of the side scan backscatter strength data are briefly introduced. Second, according to the variation features of backscatter strengths, the proposed 1D-CNN model is designed and then trained by using the established sample sets for recognizing bottom sequences. Third, the bottom tracking of side scan data is implemented by traversing each ping to use the trained model to detect the bottom data sequences. Lastly, the proposed method is validated in the experiment by using the measured side scan data.
