• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.135-140
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• 2009

In this paper, we synthesized active sonar target signals based on highlights model, and then carried out target classification using the synthesized signals. If the target aspect angle is changed, the different signals are synthesized. To know the result, two different experiments are done. First, The classification results with respect to each aspect angle are shown. Second, the results in two group in aspect angle are acquired. Time domain feature extraction is done using matched filter and envelope detection. It shows the pattern of each highlights. Artificial neural networks and multi-class SVM are used for classifying target signals.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.273-278
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• 2017

In this paper, a post-processing algorithm to suppress reverberation for continuous-wave active sonar system is developed. The developed algorithm is designed for a low-doppler environment where the target echo is not distinguishable from the reverberation. The algorithm is developed based on overlapping nonnegative matrix factorization method. The algorithm analyzes the frequency characteristics of transmitting ping signal, then suppresses the reverberation using time-frequency characteristics of the received signal. Simulations performed in order to evaluate the proposed algorithm, and the results show that the proposed algorithm makes 6 dB signal-to-reverberation ratio enhancement in various reverberation energy conditions.

• 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.20 no.3
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• pp.3-7
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• 2001

In active sonar system using CW signal, when the noise included reverberation has not the white characteristics, the CFAR detector estimates high threshold. Because of this reason it cannot detect targets and not resolve the closely spaced multiple targets. In order to solve these problems, we propose an adaptive reverberation rejection filter The proposed filter is composed of an adaptive filter and a fixed filter with its coefficients. To study the performance of the proposed adaptive reverberation rejection filter, various experiments have been performed under In moving active sonar environments. As a results, the proposed method has the improved performance than the previous methods.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.455-471
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• 2013

Active sonar has been commonly used to detect targets existing in the shallow water. When a signal is transmitted and returned back from a target, it has been distorted by various properties of acoustic channel such as multipath arrivals, scattering from rough sea surface and ocean bottom, and refraction by sound speed structure, which makes target detection difficult. It is therefore necessary to consider these channel properties in the target signal simulation in operational performance system of active sonar. In this paper, a monostatic active sonar system is considered, and the target echo, reverberation, and ambient noise are individually simulated as a function of time, and finally summed to simulate a total received signal. A 3-dimensional highlight model, which can reflect the target features including the shape, position, and azimuthal and elevation angles, has been applied to each multipath pair between source and target to simulate the target echo signal. The results are finally compared to those obtained by the algorithm in which only direct path is considered in target signal simulation.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.91-97
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• 2001

Since the new field information of target signal is important in the development and verification of active sonar system, experimental method and simulation technique are widely used in order to analyze the detail characteristics of target scattered echoes. Therefore, in this paper, the scale target experiment is performed to develope and Improve the target signal simulation model. Since the experimental results show that the specular reflection is the major component among scattering mechanisms, the target signal simulation model based on the Geometric Optics Theory (GOT) is developed. Complex target is separated into simple shapes, known as canonical shape. The contribution from individual canonical shapes are summed with proper phase and amplitude to produce the target strength of the whole complex body. Simulated target signal is compared with the experimental results and discussed.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.75-81
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• 2015

In active sonar system, clutters degrade performance of target detection/tracking and overwhelm sonar operators in ASW (Antisubmarine Warfare). Conventional clutter reduction algorithms using consistency of local peaks are studied in multi-ping data and tracking filter research for active sonar was conducted. However these algorithms cannot classify target and clutters in single ping data. This paper suggests a single ping clutter reduction approach to reduce clutters in mid-frequency active sonar system using echo shape features. The algorithm performance test is conducted using real sea-trial data in heavy clutter density environment. It is confirmed that the number of clutters was reduced by about 80 % over the conventional algorithm while retaining the detection of target.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.66-72
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• 2018

In this paper, we propose an adaptive beamforming algorithm of triplet arrays for active sonar systems. The proposed algorithm consists of three steps: matched filters, cardioid beamforming, and line array beamforming. First, we apply a matched filter of a transmitted pulse to received individual sensor signals and obtain filterd signals. Then, we perform the fast Fourier transform to the matched filter results, and make a cardioid beam for each triplet data, respectively. Finally, we apply an adaptive beamforming by assuming that the cardioid beams are input signals of a line array. Experimental results demonstrate that the proposed algorithm provides better performances than conventional algorithms.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.481-491
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• 2022

The active sonar transmission system consists of a transmitter that outputs an electrical signal and an underwater acoustic transducer that converts the amplified electrical signal into an acoustic signal. In general, the transmitter output characteristics are dependent on load impedance, and an underwater acoustic transducer, which is a transmitter load, has a characteristic that the electrical impedance varies largely according to frequency when driven. In such a variable impedance condition, the output of the active sonar transmission system may become unstable. Hence, this paper proposes a design and control technique of a sonar transmitter for transmitting a stable transmission signal even under variable impedance conditions of an underwater acoustic transducer in an active sonar transmission system. The electrical impedance characteristics of the underwater acoustic transducer are experimentally analyzed, and the sonar transmitter is composed of a single-phase full-bridge inverter, an LC filter, and a matching circuit. In this paper, the design and output control method of the sonar transmitter is proposed to protect the transmitter and transducer. It can secure stable output voltage characteristics even if it transmits the Linear Frequency Modulation (LFM) signal. The validity is verified through the simulation and the experiment.