• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.12
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• pp.1173-1180
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• 2013

High Resolution Range Profile-Jet Engine Modulation imagery first introduced in 2005 carries out radar target recognition by localizing the position of the jet engine installed on the aircraft target. This paper presents a new approach for estimating the jet engine position in the HRRP-JEM image based on the eccentricity of a complex signal. It can effectively evaluate the contribution of the JEM component to the radar received signal in a range bin of the HRRP-JEM image. Therefore, the localization is expected to be performed more quantitatively and reliably by pinpointing the range bin corresponding to the jet engine position where the JEM contribution is maximized. The simulation results of realistic aircraft models validated the effectiveness of the proposed concept.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.419-424
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• 2017

TDoA (Time Difference-of Arrival) or DoA (Direction-of-Arrival) can be used for source localization. However, the localizing performance is dependent on relative position between source and receivers, receivers' geometric structure, sound speed, and so on. In this paper we propose a source localization method with enhanced performance that combines multiple information. The proposed method uses the time TDoA, DoA and sound speed as variables. LM (Levenberg-Marquardt) method which is one of nonlinear optimizations is applied. The performances of the proposed method was evaluated by simulation. As result of simulation, the proposed method has the lower average localizing error performance than the previous method.

• The Journal of the Acoustical Society of Korea
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• v.27 no.1
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• pp.40-46
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• 2008

Weak target detection and localization in the presence of loud surface ship noise is a critical problem for matched field processing (MFP) in shallow water. For stationary sources, each signal component of received signal can be separated and interference can be suppressed using eigen space analysis schemes. However, source motion, in realistic cases, causes spreading of signal energies in their subspace. In this case, eigenvalues of target and interfere signal components are mixed and hard to be separated with usual phone space eigenvector decomposition (EVD) approaches. Our technique is based on mode space and utilizes the difference in their physical characteristics of surface and submerged sources. Performing EVD for modal cross spectral density matrix, interference components in the mode amplitude subspace can be classified and eliminated. This technique is demonstrated with synthetic data, and results are discussed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.3
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• pp.82-89
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• 2012

This paper presents the intelligent tracking algorithm for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. Fuzzy c-mean clustering and predicted impact point are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by fuzzy c-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. The filtering process in a series of the algorithm which estimates the target value recognize the nonlinear maneuvering target as linear one because the filter recognize only remained noise by extracting acceleration from the positional error. After filtering process, we get the estimates target by compensating extracted acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. To maximize the effectiveness of the proposed system, we construct the multiple model structure. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.166-172
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• 2007

In this paper, we address the target positioning performance of Multi-Static sonar with respect to target positioning method and measurement error. Based on the analysis on two candidate solution approaches, namely, Least Square (LS) using range and angular information simultaneously and Maximum Likelihood (ML) using only range information as the existing information fusion methods for possible application to Multi-Static sonar, we propose to employ ML using range and angular information. Assuming that each sensor can receive range and angular information, we conduct representative comparison experiments over the existing and proposed methods under various measurement noise scenarios. We also investigate the target positioning performance according to number of sensors, distance between transmitter and receiver. According to the experimental results, RMSE of the proposed ML with distance and direction information is found to be more superior to ML using distance alone and to LS in case distance between transmitter and receiver is longer and number of receiver is smaller.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2000.10a
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• pp.27-28
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• 2000

일반적으로 과학어군탐지기는 임의의 수층에서 어군에 의해 산란된 신호로부터 자원량 추정을 실시한다. 따라서, 대상 어군을 구성하는 개체어의 표적강도(Target Strength ; TS)는 가장 중요한 변수이며, 이로부터 현장에서 어군의 분포 밀도를 좀더 정확하게 추정할 수 있다. TS를 결정하는 변수는 자세각, 어체의 체장, 부레의 유무, 주파수 등이다. TS를 알기 위한 많은 국내ㆍ외의 실험들이 죽은 어체를 이용하였으며, 살아있는 어체라도 음축으로부터 어체의 위치를 정확히 파악하기 어려운 센서들을 사용하여 정확한 TS의 정보를 제공하는데는 한계가 있었다. (중략)

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.8
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• pp.758-765
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• 2016

This paper proposes a novel method for the recognition of the inverse synthetic aperture radar(ISAR) image of multiple targets flying in formation. Rather than separating the ISAR image of each target, the proposed method combines an ISAR image obtained by fusing the ISAR images in the training database. Fusion is conducted by optimizing the non-linear problem whose parameters are the aspect angle and the target location. Assuming that the aspect angle is properly estimated, the proposed method estimates the number of the targets and their locations by optimizing the template matching using PSO. In simulations using the F-16 scale model, the efficiency of the proposed method was demonstrated by yielding the ISAR image identical to that of targets in formation.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.