• Journal of Positioning, Navigation, and Timing
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• v.3 no.2
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.12C
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• pp.1201-1207
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• 2005

When the isolation between transmitter and receiver in Electronic Warfare equipment is not sufficient, the radiated noise jamming signal from the transmitter feeds back into the receiver and interferes with receiving radar pulse signal. Therefore pulse jamming and noise jamming can't be performed together in the same frequency bands. In this paper, we present a time-sharing TX/RX control technique of the switch matrix which inhibits the transmission of noise jamming signal by using the predicted gate of pulse train and also makes the corresponding channel filter operate to receive the radar pulse signal during the predicted gate pulse. This technique was implemented by EPLD and confirmed by experiment. The proposed technique enables the pulse jamming and the noise jamming to be simultaneously executed in multiple jamming environments.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.3
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.955-961
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• 2021

In modern warfare, the importance of electronic warfare, which carries out a mission that using radio wave to find out enemy information or to protect ally information, has increased. Radar jamming technique is one of the most representative techniques of EA(Electronic Attack), it disturbs and deceives enemy radar system in order to secure ally location information. Velocity deception jamming technique, which is one of the radar jamming techniques, generally operate against pulse-doppler radar which use doppler effect in order to track target's velocity and location. Velocity Deception Jamming Technique can be implemented using DRFM(Digital Radio Frequency Memory) that performs Frequency Modulation. In this paper, I describe implementation method of VGPO/VGPI(Velocity Gate Pull-Off/Pull-In) velocity deception jamming technique using phase-sampled DRFM, and verify the operation of VGPO/VGPI velocity deception jamming technique with board test under signal injection condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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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%.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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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.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.5
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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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• v.11 no.3
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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.