*4.5. Development of Modern Scanning Sonars*

With the development of modern sonar technologies including interferometry, the newest scanning sonar could not only obtain sonar images but also bathymetric data [29] including the following.


Although these sonars have many advantages, they are only used by a limited number of companies and research institutions because of their high cost.

The traditional side scan sonar remains one of the most widely used marine survey instruments because of its very low cost. Moreover, modern data process algorithms may provide new abilities for the traditional side scan sonar. In this paper, by using a real-time bottom tracking algorithm, the side scan sonar can measure the seabed depth. This enhances the potential applications of the side scan sonar.

### *4.6. Handling of Important Issues*

The following important issues should be noted.

Low SNR. Our method processes side scan data that have been compensated and converted in fixed ranges when most information (e.g., the original signal level and time-varied gain) is unavailable. Under this situation, the echo intensities are almost in the same range. When the SNR is very small, the echo intensities of the bottom can be affected by noise, but the variation features remain. We believe that our method can process side scan data with very small SNR after training of the corresponding samples.

Obstacles in the water column. When obstacles (e.g., the fish school) exist above the seabed, the fishes can be easily distinguished by using the trained 1D-CNN with enough negative samples (i.e., fishes). By training with all types of obstacle samples, the network can distinguish the bottom, the fishes, and the other obstacle targets from one another. Moreover, when the bottom continuity hypothesis fails, our method will automatically search for the new bottom position.

Reproducibility. Each step of our method is described in detail, including how to create the samples from the recorded side scan data, how to design a suitable 1D-CNN, how to train the network, and how to use the proposed bottom tracking method. In the experiment, we demonstrate our complete processing procedure, including the sampling, training, and bottom tracking. We believe that the reader can easily reproduce our results by using their own side scan data.

## **5. Conclusions**

Based on the 1D-CNN recognition of bottom backscatter strength sequences, this paper develops a high-accuracy and real-time bottom tracking method of side scan sonar data. This method was validated by using the measured side scan data from Meizhou Bay, and the validity of each step of this method was proven. The side scan sonar data from the experimental area were bottom tracked by using the proposed method, and the average bottom tracking accuracy reached 94.7% with a 4.5% miss-ping rate, and 99.2% excluding the missing data. The experimental results showed that the proposed method is highly robust to the effects of noise, rich seabed texture, and artificial targets and proved its accuracy and real-time performance. Our method can process side scan data in field measurements (i.e., when the operator has no control over the SNR, or when fishes or obstacles are present in the water column, or in analogue simulations by using the recorded data), and in post-processing (i.e., when the recorded data only contain compensated and converted backscatter strengths). The proposed method also demonstrates that the real-time sound is possible by using the side scan sonar, which may further expand the applications of side scan sonars.

**Author Contributions:** Conceptualization, J.Y. and J.M. Methodology, J.Y. Software, J.Y. Validation, J.Y., J.M., and J.Z. Formal analysis, J.Y. and J.M. Investigation, J.Y. and J.Z. Resources, J.Z. Data curation, J.Y. Writing—original draft preparation, J.Y. Writing—review and editing, J.Y., J.M., and J.Z. Visualization, J.Y. and J.M. Supervision, J.Y. and J.Z. Project administration, J.Y. and J.M. Funding acquisition, J.Y., J.M., and J.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** The National Natural Science Foundation of China (grant number 41906168, 41576107, and 51804001), Natural Science Foundation of Anhui Province (grant number 1908085QD161), and the University Science Research Key Project of Anhui Province (grant number KJ2019A0024) funded this research.

**Acknowledgments:** The Guangzhou Marine Geological Survey Bureau provided the data in this study. The authors are appreciative of their support. We gratefully thank the editor and the anonymous reviewers for their valuable comments and suggestions that greatly improve our manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.
