• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.15-19
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• 2006

In shallow water active sonar environment, reverberation which is a non-white noise is one of the main source of performance degradation of target detection. In this case, the received signal is whitened before applying matched filter known as an optimum filter in the presence of white noise. However implementation of this method is very difficult because of the non-stationary characteristic of reverberation. Traditionally reverberation is assumed local stationary. In this paper, we estimate a range of stationary of reverberation signal, and then propose a pre-whitening method which improve the performance of pre-whitening block normalized matched filter in presence of non-white reverberation noise. Proposed whitener shows better whitening performance than traditional whitener because it use later as well as before reverberation of target signal. To evaluate performance of the proposed whitener, an actual measurement data sampled at the East-Sea is used for computer simulation. The target detector with new whitener is shown better performance than detector with traditional whitener.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2
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• pp.30-37
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• 1994

Since the morden targets are of high speed and getting quiet in both active and passive mode, the necessities of developing advanced SONAR system capable of performing target motion analysis (TMA) and target classification are evident. In order to develop such a system, the scattering mechanism of complex bodies needs to be, some extent, fully understood and modeled. In this paper, MOving Spread Target(MOST) signal simulation model is presented and discussed. The model is based on the highlight distribution method, and simulates pulse elongation of spread target, doppler effect due to kinematics of the target as well as SONAR platform, and distribution target strength of each highlight point (HL) with directivity. The model can be used in developing and evaluating advanced SONAR system through system simulation, and can also be used in the development of target state estimation algorithm.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.534-542
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• 2019

In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.172-180
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• 2012

In multi-static sonar for detecting an underwater target, received signals contain the target echo, reverberation and clutter. Clutter and reverberation are main causes of increasing the false alarm rate. MRAL classifies received signals according to the spatial similarity, and it regards classified signal as reflected signals from a reflector. MRAL reduces the false alarm rate this way. However, the results of MRAL can have localization errors. In this paper, an MRAL post processing algorithm is proposed to reduce the localization errors with the least square (LS) method.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.38-43
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• 2017

Many studies about generating images of seabed using active sonar have been carried out but image resolution enhancement is still an important problem. Many methods have been proposed to improve sonar resolution and the approach using narrow beam width is commonly and widely applied to enhance azimuth resolution. Unfortunately, this has technical limitations to reduce the beam width. Therefore, signal processing techniques are essential to achieving higher azimuth resolution when an array with conventional beam width is employed. This paper proposes a new approach that utilizes overlapped beams to obtain higher resolution.

• 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.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.683-686
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• 1999

In this paper, we develop an active sonar signal synthesis model to analyze the detection performance of active sonar systems in a shallow water environment. Using this model, we synthesize the return signal of a bistatic sonar system at a typical operating frequency. This signal can be used to test proper active sonar signal processing techniques for real applications.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.49-59
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• 2022

The SHAPE algorithm has the advantage of being able to shape the pulse spectrum as desired and design it not to distort other characteristics, so it was used in the active sonar pulse design. In this paper, we propose a pulse design using the SHAPE algorithm for a multi-static sonar system to reduce the cross-correlation between frequency-adjacent pulses and prevent the performance degradation of the pulses themselves. The boundary function of the SHAPE algorithm is set to be limited to the pulse bandwidth. As a result of applying the proposed design method to the sinusoidal frequency modulated pulse, the peak cross-correlation level (PCCL), which means the degree of cross-correlation, was reduced by 44.23 dB. Although the PCCL decreased by several tens of dB, no significant change in the ambiguity function was observed, and the integrated sidelobe level (ISL), which means the average value of the side lobe, increased by 11.64 dB.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3
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• pp.129-135
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• 2007

In this paper. we propose a prewhitening method for the km reverberation to enhance the target signal. The proposed algorithm uses the dechirping method which inversely compensates the frequency chirp rate of LFM and transforms the LFM reverberation to have stationary frequency property in each data block. Also, using the left and right adjacent beam signals as reference signals. we model frequency response of each data block by AR coefficients. From these coefficients, we implement inverse filter and prewhiten the LFM reverberation of the center beam efficiently.