콘볼루션 신경회로망을 이용한 능동펄스 식별 알고리즘 = Active pulse classification algorithm using convolutional neural networks

다국어 초록 (Multilingual Abstract)

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

참고문헌 (Reference)

1 김근환, "비협동 양상태 소나 시스템을 위한 펄스식별 자동화 기법 연구" 한국군사과학기술학회 21 (21): 158-165, 2018

2 H. Schmidt-Schierhorn, "The use of bistatic sonar functions on modern submarines" 5-7, 2007

3 D. H. Lee, "Source information estimation using enemy’s single-ping and geographic information in non-cooperative bistatic sonar" 12 : 2784-2790, 2012

4 A. Krizhevsky, "Imagenet classification with deep convolutional neural networks" 1097-1105, 2012

5 H. Cox, "Handbook of Underwater acoustic data processing" Springer 1989

6 J. Schmidhuber, "Deep learning in neural networks: An overview" 61 : 85-117, 2015