• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.9-16
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• 2011

When a hydrofoil ship plies at high speed, there exist possibilities of collision with ocean mammals dwelling near the surface. An active sonar located within the immersed hydrofoil structure that provides the lift for the vessel, can be used for early warning of their presence. The proper functioning of the active sonar system depends on its ability to reject noise and pick up the target signal. In this article, we measured the noise on a hydrofoil of an operating ship with two flush-mounted hydrophones. The measurements were conducted for the purpose of (1) identifying the effect of operating state of machinery likes engine, cooler and generator (2) observing the change of noise depending on the measuring position (3) observing the change of noise with increasing ship speed. To verify our experiment, experiments were performed three times and the measured results are compared with other investigations and they show similarity to each other. The results are analyzed with frequency domain in order to apply to operating active sonar detecting system and focus on high frequency band within sonar's operating frequency region. Through these experiments and analysis, it is expected that we can identify the generated noise around hydrofoil where active sonar is installed and these results lead us to design active sonar that could distinguish target signal from noise more effectively.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1083-1088
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• 2013

Detection and classification of undersea mines in shallow waters using active sonar returns is a difficult task due to complexity of underwater environment. Support vector machine(SVM) is a binary classifier that is well known to provide a global optimum solution. In this paper, classification experiments of sonar returns from mine-like objects and non-mine-like objects are carried out using the SVM, and classification performance is analyzed and presented with discussions depending on parameter values of SVM kernel functions.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.2
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• pp.83-93
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• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.1
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• pp.65-70
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• 1997

An active sonar is used to the navigation system or military purposes. In the active sonar one of the problems is a reverberation. The reflected signals from surface, bottom, and volume are received at receiver. This reverberation is an interference in the active sonar, and for the enhanced performance must be rejected. In this paper I study the method to reject the reverberation. The proposed method use the orthogonal property between the signal subspace and noise subspace in the eigen subspace. In the proposed method the noise subspace is calculated. I have performed the computer simulations to prove the performance of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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• pp.739-747
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• 2010

In this paper, we propose new data association method called the Highest Probability Data Association(HPDA) using a Signal Amplitude information ordering method applied to active sonar tracking and track initiation in cluttered environment. The performance of HPDA is tested in a series of Monte Carlo simulations runs and is compared with the existing Probabilistic Data Association with Amplitude Information(PDA-AI) for active sonar tracking in clutter. The proposed HPDA algorithm is also applied to automatic track initiation in clutter and its performance is compared with the existing IPDA-AI algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.673-678
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• 2009

The efficiency of active sonar that is used underwater observation equipment is important for obtain the information of topography and trace for the objects. Sound wave transmitted from sonar are distorted by acoustic window which is to protect sonar. Making various sonar dome is impossible for experiment, because consumed unnecessary time and expense. So, the purpose of this study is to simulate and analyze the acoustic window propagated sound wave from sonar for designing model reduced insertion loss. Simulation is performed by transient analysis and fluid-structure interaction analysis. As a result, this study will give a opportunity for efficient design of sonar dome without high cost and time consumption.

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.460-465
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• 2021

In active sonar, time delay estimation is used to find the distance between the target and the sonar. Among the time delay estimation methods for active sonar, estimation in the frequency domain is widely used. When estimating in the frequency domain, the time delay can be thought of as a frequency estimator, so it can be used relatively easily. However, this method is prone to rapid increase in error due to noise. In this paper, we propose a new method which applies weighted dictionary and sparsity in order to reduce this error increase and we extend it to two receivers to propose an algorithm for estimating the time delay between two receivers. And the case of applying the proposed method and the case of not applying the proposed method including the conventional frequency domain algorithm and Generalized Cross Correlation-Phase transform (GCC-PHAT) in a white noise environment were compared with one another. And we show that the newly proposed method has a performance gain of about 15 dB to about 60 dB compared to other 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.