• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.761-767
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• 2009

The detection of target echoes in a sonar image is usually difficult since reverberation is originated by the returns reflected around the boundary and volumes. Under the scenario of the target presence around the reverberation, the detection performance of existing algorithms is degraded. Since they have a similar statistical features. But proposed detector gives improvement existing algorithms Under this scenario. In this paper, 4 segmentation contrast box algorithm using maximum contrast value is proposed based on statistical segmentation, which gives better detection performance in the sense of reducing false alarms. The simulations validate the effectiveness of the proposed algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2137-2142
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• 2012

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has a difficult in collecting actual underwater data. In this paper, we implemented the simulator to synthesize the active target signal, to extract feature and to classify the target in the underwater environment. In target signal synthesis, highlight and three-dimensional model are used and multi-aspect based hidden markov model is used for target classification.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06d
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• pp.84-87
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• 1998

소음원 탐지는 환경 소음제어, 음향 표적 탐지 및 음성 통신 등의 광범한 분야에 적용되는 연구분야로 Beamforming 기술, 상관함수법, 음향인테시티법등 다양한 기술이 적용되는 분야이다. 본 연구에서는 최근 그 응용 범위가 증대고고 있는 Matched Filterig 기술을 이용한 소음원 탐지기술의 수치 해석 결과로 종래 연구가 현상적인 특성의 1차적 응용이라면 본 연구는Matched filtering 의 공간 분해능 특성을 해석한 것으로 배열 중심선과 소음원이 이루는 경사각에 따른 분해능 특성을 중심으로 논의되었다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.269-272
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• 2000

수중 표적으로부터 수신된 신호에서, 파도와 난류 등의 영향으로 선박의 특정 기계류들의 부하변동과 도플러효과 등에 의해 야기되는 특징 주파수의 시간적 변동과 같은 음향 특징 추출은 수중음향 신호처리에서 중요한 연구분야이다. 본 연구에서는 이들의 발생기구 해석을 기초로 효과적인 분석 추적 기법을 제시한다.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.218-227
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• 2017

In this paper, an algorithm to calculate both bearing and distance error for target detection and localization is proposed using the Cramer Rao lower bound to estimate the minium variance of their error in DOA (Direction Of Arrival) estimation. The performance of arrays in detection and localization depends on the accuracy of DOA, which is affected by a variation of SNR (Signal to Noise Ratio). The SNR is determined by sonar parameters such as a SL (Source Level), TL (Transmission Loss), NL (Noise Level), array shape and beam steering angle. For verification of the suggested method, a Monte Carlo simulation was performed to probabilistically calculate the bearing and distance error according to the SNR which varies with the relative position of the target in space and noise level.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.163-169
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• 2020

The asynchronous bistatic sonar needs to estimate direct blast arrival time at a receiver to localize targets, and therefore the direct blast arrival time estimation error could be added to target localization error in comparison with synchronous system. Direct blast especially appears as several peaks at the matched filter output by multipath, thus we compared the first peak detection technique and the maximum peak detection technique of those peaks for direct blast arrival time estimation through sea trial data. The test was performed in a shallow sea with bistatic sonar made up of spatially separated source and line array sensors. Line array sensors obtained the target signal which is generated from the echo repeater. As a result, the first peak detection technique is superior to maximum peak detection technique in direct blast arrival time estimation error. The result of this analysis will be used for further research of target tracking in the asynchronous bistatic sonar.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.17-25
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• 2001

Traditional adaptive noise cancelling methods rely their performance on various interfering parameters, such as convergence speed, tracking ability, numerical stability, relative frequency characteristics between target and reverberation signals, and activity of the target. In this paper, an adaptive noise cancelling method is suggested, which Provides a successful tradeoff mon these factors. It is designed to work on the transform domain, adopts the Gram-Schmidt orthogonalization process, and is implemented by the escalator algorithm. The transform domain approach supports a tradeoff between the convergence speed and numerical cost. The proposed method is verified by applying a real-data collected in the shallow waters off the east coasts of korea. It is shown that it has a good reverberation-rejection capability even for the target signal with adjacent frequency components to those of the reverberation, and its performance is invariant for the activity of the target.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.110-117
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• 2016

Passive sonar systems detect and classify the target by analyzing the radiated noises from vessels. If multiple noise sources exist within the sonar detection range, it gets difficult to classify each noise source because mixture of noise sources are observed. To overcome this problem, a beamforming technique is used to separate noise sources spatially though it has various limitations. In this paper, we propose a new method that uses a FDICA (Frequency Domain Independent Component Analysis) to separate noise sources from the mixture. For experiments, each noise source signal was synthesized by considering the features such as machinery tonal components and propeller tonal components. And the results of before and after separation were compared by using LOFAR (Low Frequency Analysis and Recording), DEMON (Detection Envelope Modulation On Noise) analysis.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.401-407
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• 2021

The continuous active sonar technology is effective for detecting and tracking targets because of short target revisiting rate. Generalized Sinusoidal Frequency Modulation (GSFM) pulses suitable for continuous active sonar systems are known to be capable of obtaining high time-bandwidth product while maintaining the orthogonality between pulses. However, it is unknown how to calculate an appropriate length of time to correlate received GSFM pulses in the presence of a target with acceleration. In this paper, we propose a method to calculate the appropriate time length based on the correlation when matching the received signal in the continuous active sonar system using GSFM pulse. The proposed method calculates the correlation according to the acceleration of the target and calculates the signal processing length according to the correlation. It is shown that stable detection performance can be obtained when the signal processing length calculated by the proposed method through the level of the sidelobe is applied.

• 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.