• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.549-557
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• 2019

The acoustical scattering characteristics of a target are influenced by the material properties and structural characteristics of the target, which are critical information for acoustic detection and identification of underwater target. In particular, for thin elastic target, unique scattered signals are generated around the target by the Lamb wave. In this paper, the results of scattered signal measurement of aluminum spherical shell in the water tank using the stepped frequency sweep sine signal are presented. In particular, the scattering of the water-filled aluminum spherical shell is compared with that of the air-filled one both theoretically and experimentally. The difference of the scattered signals are analyzed using the pseudo Wigner-Ville distribution in terms of average frequency, frequency distribution, and energy of the scattered signal. The result shows that all observed parameters increased when the aluminum sphere was water-filled, and it is well matched to the theoretical expectation.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2
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• pp.87-94
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• 2007

In active sonar system, aspect angle and length of a target can be estimated by calculating the cross-correlation between left and right split-beams of a LFM(Linear Frequency Modulated) signal. However, high-resolution performances in bearing and range are required to estimate the information of a remote target. Because a certain higher sampling frequency than the Nyquist sampling frequency is required in this performance, an over-sampling process through interpolation method should be required. However, real-time implementation of split-beam processing with over-sampled split-beam outputs on a COTS(commercial off-the-shelf) DSP platform limits its performance because of given throughput and memory capacity. This paper proposes a cumulative processing algorithm for split-beam processing to solve the problems. The performance of the proposed method was verified through some simulation tests. Also, the proposed method was implemented as a real-time system using an ADSP-TS101.

• Journal of Korea Multimedia Society
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• v.19 no.10
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• pp.1775-1781
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• 2016

Generally, in the underwater target recognition, feature vectors are extracted from the target signal utilizing spatial information according to target shape/material characteristics. In addition, various signal processing techniques have been studied to extract feature vectors which are less sensitive to the location of the receiver. In this paper, we synthesized active echo signals using 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to echo signals to extract signal features. For the performance verification, classification experiments were performed using backpropagation and probabilistic neural network classifiers based on single aspect and multi-aspect method. As a result, we obtained a better recognition result using proposed feature extraction and multi-aspect based method.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1637-1645
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• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.