• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.159-162
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• 2002

This paper deals with target positioning performance according to active sonar formation and measurement error. Generally, active sonar can be categorized into Monostatic, Bistatic and Multistatic cases and their error characteristics are different each other. In this paper, on the assumption that each receiver has two kinds of measurements； sum of distances, and a angle between receiver and target, we suggest least square(LS) method that combines the two measurements in Multistatic formation, and compare Multistatic case with Monostatic and Bistatic cases. Experimental results show that target positioning RMSE in Multistatic sonar is superior to those in Monostatic and Bistatic sonar by approximately 57％.

• 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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.186-189
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• 1993

능동 소나 체계에서 표적의 탐지거리 예측을 위하여 소나방정식이 이용되는데, 이는 음원 준위, 전달 손실, 표적 강도, 복반사 준위, 소음 준위, 방향성 이득, Detection threshold, Signal excess, 탐지 확률과 탐지거리의 요소로 구성된다. 본 연구에서는 능동 소나 체계에서 소나 깊이와 표적 깊이의 함수인 탐지거리를 계산하기 위한 알고리즘에 대해 살펴보았다. 소나의 각 요소와 환경이 주어졌을 때 SAFARI 모델을 이용하여 각 수신기의 깊이와 거리에서의 전달손실을 계산하였으며, 구하여진 전달 손실과 배경 소음 준위를 이용하여 Signal excess를 계산하였다. ROC(Receiver-operating-characteristic) 곡선을 이용하여 Signal excess를 탐지 확률로 계산한 후 두 항을 곱하여 각 깊이별 거리로 적분함으로서 탐지거리를 구하였다. 주파수 30Hz의 전방향 음원을 사용하여 여름의 일반적 음속 분포에서 계산한 결과 100m 음원 보다 300m 음원에서 상대적으로 큰 탐지거리를 얻었으며 각 음원 깊이별 평균 탐지거리는 100m 이하의 표면을 제외한 500m 까지는 거의 일정함을 알 수 있었다.

• 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.35 no.2
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• pp.149-157
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• 2016

In this paper, we propose a new clutter reduction algorithm, which rejects heavy clutter density in shallow water environments, based on a clustering method. At first, it applies the density-based clustering to active sonar measurements by considering speed of targets, pulse repetition intervals, etc. We assume clustered measurements as target candidates and remove noise, which is a set of unclustered measurements. After clustering, we classify target and clutter measurements by the validation check method. We evaluate the performance of the proposed algorithm on synthetic data and sea-trial data. The results demonstrate that the proposed algorithm provides significantly better performances to reduce clutter than the conventional algorithm.

• 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.22 no.3
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• pp.224-232
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• 2003

In this paper, efficient deployment method of sensors and target positioning performance with respect to measurement error are dealt with. Active sonar can be categorized into Monostatic, Bistatic, Multistatic sonar, and characteristics of respective sonar are different. Assuming that each sensor can receive range and angular information, we compare the performance of Monostatic, Bistatic, and Multistatic systems. And we suggest Weighted least square (WLS) which gives the weight to former case, LS. In particular. adopting suggested method we investigate the target positioning performance according to number of sensor, distance from transmitter to receiver, and propose efficient arrangement rule for Multistatic sonar configurations. According to the experimental results, RMSE of Multistatic sonar is found to be superior to Monostatic and Bistatic by 35.98%. 37.45% respectively, and WLS is superior to LS approximately by 7.4% in average. Furthermore, as the difference of respective sensor's variance is large, it is observed that the improvement ratio of target positioning performance is increased.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.329-344
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• 2023

In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

• 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.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.