• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.47-54
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• 2016

In the underwater environment, high resolution can be achieved in the range direction by transmitting and receiving a signal of a particular band and/or waveform. The design of a transmit signal used in the active sonar is very important in order to detect a cylindrical object within a short distance less than 1 km, which is the detection distance of this paper. Designing a transmit signal optimal to a sonar requires appropriate selection of its center frequency and bandwidth, which allows the maximum detection distance of a sonar. In this paper, in terms of maximizing echo excess and signal to noise ratio (SNR), optimum frequency analysis is carried out under various conditions of diverse parameters. In addition, the investigation focused on the determinating a bandwidth is also performed for the purpose of satisfying the performance requirement of range resolution and azimuth resolution.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2569-2574
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• 2012

HMS(Hull Mount Sonar) equipment mounted on PCC(Patrol Combat Corvette) is suitably designed for active mode, and the specific character of sensor or system is not appropriate for the frequency range to detect a torpedo. In this article, in order to implement the function of detecting torpedoes with HMS of existing PCC, I will analyze the feature of input signals each PCCs and design a circuit to compensate reversely for the input signal in certain frequency. And also, I will suggest the most adequate torpedo defense system suitable for the special operating environment and the characteristic of naval vessels, implementing functions such as AGC of input signal and fixing the frequency range of different input signals per different warships.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2505-2511
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• 2013

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 difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using neural network classifier.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.96-105
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• 2019

Ocean reverberation is the most limiting factor in designing realistic and real-time system for sonar simulator. The simulation for an ocean reverberation requires a lot of computational loads, so it is hard to embed program and generate real-time signal in the sonar simulator. In this study, we simulate a time-domain bottom reverberation signal based on Harrison's energy-flux bottom reverberation model by applying Doppler effects as ship maneuvering and autoregressive model. Finally, the bottom reverberation signal with realistic characteristics could be generated for the simulation of ONR reverberation modeling workshop-I problem XI and East Sea ocean environments.

• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.58-66
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• 2001

In this paper, a computationally efficient time delay and doppler estimation algorithm is proposed for active sonar with Linear Frequency Modulated (LFM) signal. To reduce the computational burden of the conventional estimation algorithm, an algebraic equation is used which represents the relationship between the time delay and doppler in cross-ambiguity function of the LFM signal. The algebraic equation is derived based on the Fast maximum Likelihood (FML) method. Using this algebraic relation, the time delay and doppler are estimated with two 1-D search instead of the conventional 2-D search. The estimation errors of the proposed algorithm are analyzed for various SNR's. The simulation result demonstrates the good performance of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.93-100
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• 2000

Target signal feature parameters are very important to classify target by active sonar. Two highly correlated broad band pulses separated by time T have a time separation pitch(TSP) of 1/T Hz which is equal to the trough-to-trough or peak-to-peak spacing of its spectrum. In this study, TSP informations which represent feature of each target signal were effectively extracted by the FFT. The extracted TSP feature parameters were also applied to the pattern recognition algorithm to classify target and to analyze their properties.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.532-537
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• 2015

In this paper, we propose target range estimation method using ghost target in the submarine linear array sonar. Usually, when submarine detect target, they use passive sonar detection to avoid self-disclosure by active sonar transmission. But, originally, passive linear array sonar have limitation for target range estimation and additional processing is required to get target range information. For the case of near-field target, typical range estimation method is using multiple information by multipath effect in underwater environment. Acoustic signal generated from target are propagated along with numerous multipath in underwater environment. Since multipath target signals received in the linear array sonar have different conic angles each other, ghost target is appeared at the bearing different with real target bearing and sonar operator can find these information on the operation console. Under several assumption, this geometric properties can be analysed mathematically and we get the target range by derivation of this geometric equations using measured conic angles of real target and ghost target.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.489-498
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• 2018

In this paper, a target detection method based on beamspace-domain multichannel nonnegative matrix factorization is studied when an echo of continuous-wave ping is received from a low-Doppler target in reverberant environment. If the receiver of the continuous-wave active sonar moves, the frequency range of the reverberation is broadened due to the Doppler effect, so the low-Doppler target echo is interfered by the reverberation in this case. The developed algorithm analyzes the multichannel spectrogram of the received signal into frequency bases, time bases, and beamformer gains using the beamspace-domain multichannel nonnnegative matrix factorization, then the algorithm estimates the frequency, time, and bearing of target echo by choosing a proper basis. To analyze the performance of the developed algorithm, simulations were performed in various signal-to-reverberation conditions. The results show that the proposed algorithm can estimate the frequency, time, and bearing, but the performance was degraded in the low signal-to-reverberation condition. It is expected that modifying the selection algorithm of the target echo basis can enhance the performance according to the simulation results.

• 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 the Korea Institute of Military Science and Technology
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• v.14 no.1
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• pp.123-131
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• 2011

In this paper, modeling and design of a distributed detection system are considered for an active sonar sensor network. The sensor network has a parallel configuration and it consists of a fusion center and a set of receiver nodes. A system with two receiver nodes is considered to investigate a theoretical aspect of design. To be specific, AND rule and OR rule are considered as the fusion rules of the sensor network. For the fusion rules, it is shown that a threshold rule of each sensor node has uniformly most powerful properties. Optimum threshold for each sensor is obtained that maximizes the probability of detection given probability of false alarm. Numerical experiments were also performed to investigate the detection characteristics of a distributed detection system with multiple sensor nodes. The experimental results show how signal strength, false alarm probability, and the distance between nodes in a sensor field affect the system detection performances.