• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.3
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• pp.34-42
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• 2007

It is very important for the combat system to process extensive data exactly at short time for the better situation awareness compared with the threats in these days. This paper suggests to add radial velocity on the decision factor of sensor data fusion in the existing algorithm for the accuracy enhancement of the sensor data fusion in the combat system.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.1
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• pp.66-74
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• 2008

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection. Scale invariant feature using the Laplacian scale-space can detect different sizes of targets robustly compared to the conventional spatial filtering methods with fixed kernel size. Additionally, scale-reflected adaptive thresholding can reduce many false alarms. Experimental results with real IR images show the robustness of the proposed target detection in real world.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.49-57
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• 2005

The target localization using the line arrays buried in the seabed is a difficult problem due to the complex sea bottom characteristics and need to compensate the wave propagation effect to localize the target accurately Sound speed mismatch in the seabed causes a bias in the target bearing estimation and induces the localization error. In this paper we describe a target localization method with improved accuracy of target bearing and localization by calibration the sound speed in the seabed. The proposed algorithm is verified through the ocean data.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.441-448
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• 2013

This paper describes the development of Target Simulator developed for performance test and failure detection of Imaging Infra-Red(IIR) seeker which is one of the most important equipments in specific cruise missile systems. The simulator makes it possible to test detecting and tracking performance for target, uniformity of IIR, FOV status and spatial resolving power. Besides, it includes several self-test functions and optic axis alignment methods to improve its own reliability.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.115-118
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• 2010

본 논문은 추적레이더의 추적 오차를 예측하기위한 레이더 시뮬레이터 개발에 관한 것이다. 본 연구에서는 다중경로 전파특성, 표적 모델링, RPY 보상, DTM 지형데이터 등을 이용한 전파의 전파특성 분석을 통한 표적추적레이더의 추적 오차 요인을 분석하였다. 이를 바탕으로 추적 오차를 줄이기 위해 추적 레이더의 서보 특성과 서보 구동의 지연보상을 위한 $\alpha-\beta$ 필터를 사용하였다. 표적 추적레이더의 추적 상황을 종합적으로 고려하여 추적 상황을 정확히 예측할 수 있는 시뮬레이터를 구현 하였다.

• The Journal of Korean Institute of Information Technology
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• v.17 no.10
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• pp.57-63
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• 2019

Motion errors which are caused by several reasons, non-ideal path, errors of navigation systems, and radar system errors, have to be corrected. Motion compensation methods can compensate the motion error, but not exactly. To correct these residual errors, several autofocus methods are invented. A popular method is phase gradient autofocus (PGA). PGA does not assume specific circumstances, such as isolated point targets and shapes of errors. PGA is an iterative and adaptive method, so that the processing time is the main problem for the real time processing. In this paper, the improved method to select targets for PGA is proposed to reduce processing time. The variances of image pixels are used to select targets with high SNR. The processing of PGA with these targets diminishes the processing time and iterations effectively. The processed results with real radar data, obtained by flight tests, show that the proposed method compensates errors well, and reduce working time.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.2
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• pp.299-307
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• 2001

This paper presents a experimental study of Inverse Synthetic Aperture Radar(ISAR) imaging using Matrix Pencil(MP) method. A series of measurement for two types of target model was done in a Compact Range(CR)facility. The first target is a set of distributed slim cylinders to get a ISAR image of point-like scatterers. The second is UAV model representing a complex real target. The results show that ISAR images by MP method are better than by conventional FFT method under the realistic measurement conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.310-313
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• 2006

본 논문은 스마트 무인항공기를 개발하거나 획득시에 요구되는 전투실험 수행 중 지능화 정도에 대한 평가 및 실험방향을 제시를 위한 지능화 성숙도 모델을 제안한다. 먼저 표적탐색 소요검증 전투실험 절차를 제시하고, 지능화 정도를 4단계로 나누어 단계별 요구되는 지능수준을 제시한다. 분류된 지능수준별로 기술수준, 동작수준, 상호운용성 수준 영역의 4단계 각 수준별 요구 능력을 분석, 제시하여 지능화 정도를 측정할 수 있는 지능화 성숙도 모델을 설계한다. 마지막으로 표적탐색 소요판단을 위한 전투실험시 활용 가능한 중점분야를 지능화 성숙도 모델 영역별로 식별하고, 단계별 식별된 중점분야를 실험할 수 있는 전투실험 평가요소를 제시한다.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Magazine of the IEIE
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• v.23 no.10
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• pp.94-102
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• 1996