• Journal of Positioning, Navigation, and Timing
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• 제3권2호
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• pp.45-51
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• 2014

GNSS has the disadvantage of being vulnerable to jamming, and thus, the necessity of jamming countermeasure techniques has gradually increased. Jamming countermeasure techniques can be divided into an anti-jamming technique and a jammer localization technique. Depending on the type of a jammer, applicable techniques and performance vary significantly. Using an appropriate jamming countermeasure technique, the effect of jamming on a GNSS receiver can be attenuated, and prompt action is enabled when estimating the location of a jammer. However, if an inappropriate jamming countermeasure technique is used, a GNSS receiver may not operate in the worst case. Therefore, jammer identification is a technique that is essential for proper action. In this study, a technique that identifies a jammer based on template matching was proposed. For template matching, analysis of a received jamming signal is required; and the signal analysis was performed using a spectral correlation function. Based on a simulation, it was shown that the proposed identification of jamming signals was possible at various JNR.

• 한국통신학회논문지
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• 제30권12C호
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• pp.1201-1207
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• 2005

전자전장비는 송수신기간의 이격이 충분하지 않을 경우, 송신기에서 방사된 잡음재밍신호는 수신기로 피드백되어 레이더 펄스신호의 수신을 방해한다. 이로 인해, 동일 주파수 대역내에서 펄스재밍과 잡음재밍을 동시에 수행할 수 없다. 본 논문에서 펄스열의 예측게이트를 이용하여 잡음재밍신호를 차단하고 동시에 해당 채별 필터를 동작시켜 레이더 펄스신호를 수신할 수 있는 스위치 메트릭스를 이용한 시분할 송수신 제어기법을 제안한다. 이 기법은 전자전장비의 EPLD 내에 구현되어 실험을 통해 확인되었으며, 다중 재밍환경에서도 동시에 펄스재밍과 잡음재밍을 가능하도록 해준다.

• 한국군사과학기술학회지
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• 제16권3호
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• pp.382-389
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• 2013

In anti-jamming GPS receiver, adaptive signal processing techniques in which the radiation pattern of adaptive array antenna of elements may be adaptively changed used to reject interference, clutter, and jamming signals. In this paper, I describes adaptive signal processing technique using the sample matrix inversion(SMI) algorithm. This adaptive signal processing technique can be applied effectively to wideband/narrowband anti-jamming GPS receiver because it does not consider the satellite signal directions and GPS signal power level exists below the thermal noise. I also analyzed the effects of covariance matrix sample size and diagonal loading technique on the system performance of five-element circular array antenna. To attain near optimum performance, more samples required for calculation covariance matrix. Diagonal loading technique reduces the system nulling capability against low-power jamming signals, but this technique improves robustness of adaptive array antenna.

• 한국군사과학기술학회지
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• 제8권4호
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• pp.91-101
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• 2005

In this paper, a technique for the quantitative analysis of the noise jamming effect is proposed. This technique based upon the mathematical modeling for noise jammers and the probability theory for random processes analyses the jamming effect by means of the modeling of the relationship among jammer, radar variables and radar detection probability under noise jamming environment. Computer simulation results show that the proposed technique not only makes the quantitative analysis of the jamming effect possible, but also provides the basis for quantitative analysis of the electronic warfare environment.

• 한국정보통신학회논문지
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• 제25권7호
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• pp.955-961
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• 2021

현대전에서는 전파를 이용하여 적의 정보를 알아내거나, 적이 탐지하려는 아군 정보를 보호하는 임무를 수행하는 전자전 분야의 중요성이 증가하고 있다. 전자전 분야의 대표적인 전자공격 방법 중 하나인 레이다 재밍기법은 적 레이다를 교란 및 기만하여 아군의 위치 정보가 노출되는 것을 방지한다. 레이다 재밍기법 중 하나인 속도기만 재밍기법은 도플러효과를 이용하여 표적의 속도와 위치를 추적하는 펄스 도플러 레이다를 대상으로 사용된다. 속도기만 재밍기법은 DRFM (Digital Radio Frequency Memory)을 이용하여 수신신호를 주파수 변조해서 송신하는 방법으로 구현할 수 있다. 본 논문은 위상 샘플방식 DRFM을 이용하여 속도기만 재밍기법 중 하나인 VGPO/VGPI 재밍기법을 구현하는 방안을 기술하고, 제작한 보드를 통해서 주입신호 환경 하에서 VGPO/VGPI 재밍기법의 동작을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4223-4249
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• 2015

A novel jamming detection technique to detect the presence of jamming in the downstream direction for cluster based wireless sensor networks is proposed in this paper. The proposed technique is deployed in base station and in cluster heads. The proposed technique is novel in two aspects: Firstly, whenever a cluster head receives a packet it verifies whether the source node is legitimate node or new node. Secondly if a source node is declared as new node in the first step, then this technique observes the behavior of the new node to find whether the new node is legitimate node or jammed node. In order to monitor the behavior of the existing node and new node, the second step uses two metrics namely packet delivery ratio (PDR) and received signal strength indicator (RSSI). The rationality of using PDR and RSSI is presented by performing statistical test. PDR and RSSI of every member in the cluster is measured and assessed by the cluster head. And finally the cluster head determines whether the members of the cluster are jammed or not. The CH can detect the presence of jamming in the cluster at member level. The base station can detect the presence of jamming in the wireless sensor network at CH level. The simulation result shows that the proposed technique performs extremely well and achieves jamming detection rate as high as 99.85%.

• 한국군사과학기술학회지
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• 제22권2호
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• 한국정보기술학회논문지
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• 제17권5호
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• pp.47-55
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• 2019

최근 GPS 재밍으로 인한 피해가 증가되면서 GPS 재밍을 탐지하고 대비하기 위한 연구가 활발히 진행되고 있다. 본 논문은 다중 GPS 수신채널과 3가지 기계학습을 이용한 GPS 재밍 탐지 방법을 다루고 있다. 제안된 다중 GPS 채널은 항재밍 기능이 없는 상용 GPS 수신기와 항잡음 재밍능력만 있는 수신기, 항잡음/항기만 재밍능력이 있는 수신기로 구성되고 운용자는 각각의 수신기에 수신된 좌표를 비교하여 재밍신호의 특성을 식별할 수 있다. 본 논문에서는 신호특성이 다른 각각의 5개 재밍신호를 입력하고, 3가지 기계학습방법(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree)을 이용하여 재밍탐지 시험을 수행하였다. 시험 결과 머신러닝 기법을 단독으로 사용하였을 때 DT 기법이 96.9% 탐지율로 가장 우수한 성능을 보였으며 이진분류기 기법에 비해 모호성 낮고 하드웨어가 단순하여 GPS 재밍탐지에 효과적임을 확인하였다. 또한, 모호성을 해결해주는 추가기법을 적용할 경우 SVM 기법을 활용할 수 있음을 확인하였다.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.910-916
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• 2010

A modern artillery use a FMCW Proximity Fuze for effectively target destruction. FMCW Proximity Fuze can be deceived by Jamming Technique because it uses RF for distance estimation. FMCW Proximity Fuze algorithm is similar to FMCW radar's, but normal Jamming Tech. like Noise and Mulitone is useless. Most Shots with FMCW Proximity Fuze have a additional mechanical fuze against RF Jamming. Shots explode by mechanical fuze when Proximity Fuse is Jammed. However, distance Deception is available because shots can not distinguish between deception jamming signal and ground reflected signal. For making Distance Deception Jamming, FMCW signal tracking is demanded. In this paper, we propose a FMCW tracking method and develop the Jammer to show Jamming signal.

• Journal of Positioning, Navigation, and Timing
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• 제11권3호
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.