• Title/Summary/Keyword: OKTAL-SE

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Three-Dimensional Conjugate Heat Transfer Analysis for Infrared Target Modeling (적외선 표적 모델링을 위한 3차원 복합 열해석 기법 연구)

  • Jang, Hyunsung;Ha, Namkoo;Lee, Seungha;Choi, Taekyu;Kim, Minah
    • Journal of KIISE
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    • v.44 no.4
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    • pp.411-416
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    • 2017
  • The spectral radiance received by an infrared (IR) sensor is mainly influenced by the surface temperature of the target itself. Therefore, the precise temperature prediction is important for generating an IR target image. In this paper, we implement the combined three-dimensional surface temperature prediction module against target attitudes, environments and properties of a material for generating a realistic IR signal. In order to verify the calculated surface temperature, we are using the well-known IR signature analysis software, OKTAL-SE and compare the result with that. In addition, IR signal modeling is performed using the result of the surface temperature through coupling with OKTAL-SE.

IR and SAR Sensor Fusion based Target Detection using BMVT-M (BMVT-M을 이용한 IR 및 SAR 융합기반 지상표적 탐지)

  • Lim, Yunji;Kim, Taehun;Kim, Sungho;Song, WooJin;Kim, Kyung-Tae;Kim, Sohyeon
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.11
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    • pp.1017-1026
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    • 2015
  • Infrared (IR) target detection is one of the key technologies in Automatic Target Detection/Recognition (ATD/R) for military applications. However, IR sensors have limitations due to the weather sensitivity and atmospheric effects. In recent years, sensor information fusion study is an active research topic to overcome these limitations. SAR sensor is adopted to sensor fusion, because SAR is robust to various weather conditions. In this paper, a Boolean Map Visual Theory-Morphology (BMVT-M) method is proposed to detect targets in SAR and IR images. Moreover, we suggest the IR and SAR image registration and decision level fusion algorithm. The experimental results using OKTAL-SE synthetic images validate the feasibility of sensor fusion-based target detection.

A Study on HILS for Performance Analysis of Airborne EOTS for Aircraft (항공기용 EOTS 성능분석을 위한 HILS시스템 구축에 관한 연구)

  • Chun, Seungwoo;Baek, Woonhyuk;La, Jongpil
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.12
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    • pp.55-64
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    • 2013
  • In this paper, the HILS (Hardware In-the-Loop Simulation) system to analyze and to verify the performance of the targeting pod is addressed. The main functions of the targeting pod is acquiring and tracking targets to guide a LGB (Laser Guided Bomb) to the targets. For the analysis of targeting pod, the real time simulate images generation of IR and daylight cameras, sever control technology, and the analysis of laser transfer characteristics are necessary. For the real time image generation and the laser transfer characteristics analysis, off-the-shelf SDK(Software Development Kit) OKTAL-SE is used. For the servo controller, well-proven mechanism in the previous program is applied to increase servo control accuracy. To analyze the performance of a targeting pod in a realistic environment, 1553B, ARINK818 interface and etc. which are actually implemented in real combat aircrafts are applied in the system. By using the developed HILS system, the performance of currently operating targeting pods in real combat aircrafts can be analyzed and predicted. Additionally, the relationship between overall system performance and each module performance can be analyzed, the currently developed HILS system is expected to be a very useful tool to generate system development requirements of targeting pods and to reduce any possible future development risks.

Low Resolution Infrared Image Deep Convolution Neural Network for Embedded System

  • Hong, Yong-hee;Jin, Sang-hun;Kim, Dae-hyeon;Jhee, Ho-Jin
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.6
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    • pp.1-8
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    • 2021
  • In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.