• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.63-64
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• 2008

GPS의 활용 분야는 군용 항법 시스템에서 항공, 선박 등의 개인 항법 시스템으로 확장되었다. GPS가 넓은 범위에서 응용됨에 따라 Jamming과 같은 고의적인 간섭 신호의 제거에 대하여 많은 연구가 진행되었다. 그러나 기만 신호의 특성이나 기만 기법에 대한 연구는 미비하다. 본 논문에서는 기만 신호의 구조와 기만 개념을 연구하였으며 기만신호의 생성 기법으로 항법 메시지를 이용한 기법과 GPS PRN 코드를 이용하여 TOA(Time of Arrival)에 오차를 인가하는 기법을 정리하고 이중 TOA에 오차를 인가하는 방식을 GPS 소프트웨어 플랫폼에 구현하여 기만신호를 생성하였다. 또한 기만 신호 대응 기법의 개발 텐 성능 분석을 위하여 소프트웨어 GPS 수신기를 이용하여 생성한 기만 신호가 GPS 수신기에 미치는 영향을 분석하였다.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.545-557
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• 2018

The Global Positioning System (GPS) has become popular and widely used in many fields from military to civilian applications. However, GPS signals are suffered from interference due to its weak signal over wireless channel. There are many types of interference, such as jamming, blocking multipath, and spoofing, which can mislead the operation of GPS receiver. In this paper, vector-based tracking loop model with integrity check is proposed to detect and mitigate the harmful effect of interference on GPS receiver operation. The suggested methods are implemented in the tracking loop of GPS receiver. As a first method, integrity check with carrier-to-noise ratio (C/No) monitoring technique is applied to detect the presence of interference and prevent contaminated channels out of tracking channels to calculate position. As a second method, a vector-based tracking loop using Extended Kalman Filter with adaptive noise covariance according to C/No monitoring results. The proposed methods have been implemented on simulated dataset. The results demonstrates that the suggested methods significantly mitigate interference of Additive White Gaussian Noise (AWGN) and improve position calculation by 44%.

• Convergence Security Journal
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• v.18 no.4
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• pp.73-79
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• 2018

Since 1910s, the drones were mainly used for military purposes for reconnaissance and attack targets, but they are now being used in various fields such as disaster prevention, exploration, broadcasting, and surveillance of risk areas. As drones are widely used from military to civilian field, hacking into the drones such as radio disturbance, GPS spoofing, hijacking, etc. targeting drones has begun to occur. Recently, the use of drones in hacking into wireless network has been reported. If the artificial intelligence technology is applied to the drones in the military, hacking into unmanned combat system using drones will occur. In addition, a drone with a hacking program may be able to relay a hacking program to the hacking drone located far away, just as a drone serves as a wireless communication station. And the drones will be equipped with a portable GPS jamming device, which will enable signal disturbance to unmanned combat systems. In this paper, we propose security threats and the anticipated hacking scenarios using the drones on the battlespace to know the seriousness of the security threats by hacking drones and prepare for future cyberspace.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.47-55
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• 2019

As the damage caused by GPS jamming has been increased, researches for detecting and preventing GPS jamming is being actively studied. This paper deals with a GPS jamming detection method using multiple GPS receiving channels and three-types machine learning techniques. Proposed multiple GPS channels consist of commercial GPS receiver with no anti-jamming function, receiver with just anti-noise jamming function and receiver with anti-noise and anti-spoofing jamming function. This system enables user to identify the characteristics of the jamming signals by comparing the coordinates received at each receiver. In this paper, The five types of jamming signals with different signal characteristics were entered to the system and three kinds of machine learning methods(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree) were applied to perform jamming detection test. The results showed that the DT technique has the best performance with a detection rate of 96.9% when the single machine learning technique was applied. And it is confirmed that DT technique is more effective for GPS jamming detection than the binary classifier techniques because it has low ambiguity and simple hardware. It was also confirmed that SVM could be used only if additional solutions to ambiguity problem are applied.