• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.131-134
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• 2000

본 논문에서는 각 지역의 수중 음향 센서로부터 중앙의 정보 융합 센터로 전송되어진 동일한 또는 상이한 표적의 Identity 정보들을 종합해 최종적으로 표적의 Identity를 결정하는 Decision Fusion 기법을 다룬다. 기존의 연구는 표적의 속성 정보로부터 정보 융합을 통해 표적의 Identity를 선택하는 기법을 주로 다루고 있다. 그러나 본 논문에서는 기존의 연구보다 한 단계 나아가 선택된 표적의 Identity들로부터 운용자가 가장 합리적인 결정을 내릴 수 있도록 하는 표적의 Identity 결정을 위한 Decision Fusion 기법을 제안한다. 이러한 수중 음향 표적 식별 시스템에서의 Identity Decision Fusion 기법으로 Voting 기법, 센서 정보의 신뢰도를 고려한 Weighted Voting 기법, 그리고 다 기준 의사 결정 기법인 Analytic Hierarchy Process (AHP) 기법을 제안하고 그 성능을 평가한다

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.434-437
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• 2011

차세대 지능적 무기체계의 자동화를 목표로 SAR(Synthetic Aperture Radar) 영상 신호를 이용한 표적 인식률 향상을 위한 여러가지 방법들이 제안되어 왔다. 기존의 연구들은 SAR 영상의 고차원 특징을 그대로 사용했기 때문에 표적 인식의 성능저하가 있었다. 본 연구에서는 정보 획득 거리가 길고, 날씨에 제약이 없이 전천후 작전 운용이 가능하도록 레이더의 특징과 고해상도 영상을 결합한 SAR 이미지를 이용한 표적 인식률 향상 방법을 제안한다. 효과적인 표적 인식을 하기위해 고차원의 특징벡터를 저차원의 특징벡터로 축소하는 PCA(Principal Component Analysis)를 기반으로 하는 SVM(Support Vector Machine)을 사용한 표적 인식 기법을 사용하였고, PCA 기반의 SVM 분류기를 이용한 표적 인식이 SVM 만을 사용한 표적 인식보다 향상된 성능을 보인 것을 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.565-566
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• 2023

적외선 영상에서 정확하게 표적을 포착하기 위해서는 수많은 거짓 표적과 참표적을 실시간으로 구별하고, 최종적으로 참 표적 하나만을 추적 할 수 있어야 한다. 본 논문에서는 추적 게이트의 이동거리 및 이동 방향을 실시간 감시하여 추적 게이트의 이상 움직임 유무를 확인하고, 추적 필터가 설정한 임계값 대비 높은 수치로 이동하거나, 한 방향이 아닌 다양한 방향으로 움직일 경우 해당 게이트를 신속하게 제거하여 거짓 표적에 대한 추적을 방지하도록 하였다. 또한 추적 게이트 이동 거리 및 확장 크기를 동적으로 조절함으로써 표적의 크기 변화와 표적의 움직임에 강인하게 추적 필터가 동작 되도록 설계하였다.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.230-237
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• 2005

The angle-tracking of maneuvering targets is required to the state estimation and classification of targets in underwater acoustic systems. The Problem of angle-tracking multiple closed and crossing targets has been studied by various authors. Sword et al. Proposed a multiple target an91e-tracking algorithm using angular innovations of the targets during a sampling Period are estimated in the least square sense using the most recent estimate of the sensor output covariance matrix. This algorithm has attractive features of simple structure and avoidance of data association problem. Ryu et al. recently Proposed an effective multiple target angle-tracking algorithm which can obtain the angular innovations of the targets from a signal subspace instead of the sensor output covariance matrix. Hwang et al. improved the computational performance of a multiple target angle-tracking algorithm based on the fact that the steering vector and the noise subspace are orthogonal. These algorithms. however. are ineffective when a subset of the incident sources are coherent. In this Paper, we proposed a new multiple target angle-tracking algorithm for coherent and incoherent sources. The proposed algorithm uses the relationship between source steering vectors and the signal eigenvectors which are multiplied noise covariance matrix. The computer simulation results demonstrate the improved Performance of the Proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.549-558
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• 2020

In this paper, the method is discussed such that the robustness of automatic initiated narrowband target tracking is improved in passive sonar. In the case of automatic tracking initiation as target in passive sonar, due to a number of clutter, the clutter is initiated as target and tracked which prohibits the operation capability. The associated probability and information entropy of measurements, extracted from detection data, is calculated to keep going on automatic target initiation and tracking of true target, but reduce the automatic initiation and tracking of clutter. If the association probability and information entropy of the extracted measurements is satisfied for the predefined conditions, the procedure of automatic initiation begins. Using sea-trial data, simulations are executed and the results from the proposed method indicate that it keeps the automatic target initiation and tracking of true target and suppresses the automatic target initiation and tracking of clutters in contrary to the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.32 no.6
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• pp.455-471
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• 2013

Active sonar has been commonly used to detect targets existing in the shallow water. When a signal is transmitted and returned back from a target, it has been distorted by various properties of acoustic channel such as multipath arrivals, scattering from rough sea surface and ocean bottom, and refraction by sound speed structure, which makes target detection difficult. It is therefore necessary to consider these channel properties in the target signal simulation in operational performance system of active sonar. In this paper, a monostatic active sonar system is considered, and the target echo, reverberation, and ambient noise are individually simulated as a function of time, and finally summed to simulate a total received signal. A 3-dimensional highlight model, which can reflect the target features including the shape, position, and azimuthal and elevation angles, has been applied to each multipath pair between source and target to simulate the target echo signal. The results are finally compared to those obtained by the algorithm in which only direct path is considered in target signal simulation.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.433-440
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• 2015

In stochastic hyperspectral target detection algorithms, the target signal components may be included in the background characterization if targets are not rare in the image, causing target leakage. In this paper, the effect of target leakage is analysed and an improved hyperspectral target detection method is proposed by excluding the pixels which have similar reflectance spectrum with the target in the process of background characterization. Experimental results using the AISA airborne hyperspectral data and simulated data with artificial targets show that the proposed method can dramatically improve the target detection performance of matched filter and adaptive cosine estimator. More studies on the various metrics for measuring spectral similarity and adaptive method to decide the appropriate amount of exclusion are expected to increase the performance and usability of this method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.527-536
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• 2010

This paper reports a reliable GRD (Ground Resolved Distance) measurement method of using natural targets instead of the method using artificial targets. For this, we developed an edge profile extraction technique suitable for natural targets. We demonstrated the accuracy and stability of this technique firstly by comparing GRD values generated by this technique visually inspected GRD values for artificial targets taken in laboratory environments. We then demonstrated the feasibility of GRD estimation from natural targets by comparing GRD values from natural targets to those from artificial targets using satellite images containing both artificial and natural targets. The GRDs measured from the proposed method were similar to the values from visual inspection and the GRDs measured from the natural targets were similar to the values from artificial targets. These results support our proposed method is able to measure reliable GRD from natural targets.