Fig. 1. Non-cooperative bi-static sonar system.
Fig. 2. Proposed pulse classification algorithm using convolutional neural networks.
Fig. 3. Database based output layer structure.
Fig. 4. Pulse feature based output layer structure.
Fig. 5. Discretization of pulse features.
Fig. 6. Example of sea experiments data.
Fig. 7. Examples of data argumentation.
Fig. 8. Training and test reysults of database based output layer structure.
Fig. 9. Training and test results of pulse feature based output layer structure (start time).
Fig. 10. Training and test results of pulse feature based output layer structure (end time).
Table 1. Error of pulse length estimation.
References
- H. Cox, "Fundamentals of bistatic active sonar," in Handbook of Underwater acoustic data processing, edited by Chan Y. T. (Springer, Netherlands,1989).
- D. H. Lee, T. J. Jung, K. K. Lee, and M. Hyun "Source information estimation using enemy's single-ping and geographic information in non-cooperative bistatic sonar," IEEE Sensor J. 12, 2784-2790 (2012). https://doi.org/10.1109/JSEN.2012.2203454
- H. Schmidt-Schierhorn, A. Corsten, B. Strassner, S. Benen, and M. Meister, "The use of bistatic sonar functions on modern submarines," UDT EUROPE, 5-7 (2007).
- G. H. Kim, K. S. Yoon, S. Kim, E. C. Jeong, and K. K. Lee. "A study on the automatic pulse classification method for non-cooperative Bi-static Sonar system" (in Korean), J. KIMST. 21, 158-165 (2018).
- A. Krizhevsky, S. Ilya, and E. H. Geoffrey, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, 1097-1105 (2012).
- J. Schmidhuber, "Deep learning in neural networks: An overview," Neural networks, 61, 85-117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003