• 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 Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.51-57
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• 2020

For the purpose of development of anti-jamming GPS receiver we have developed an anti-jamming algorithm and its Simulink implementation model. The algorithm used here is a form of Space-Time Adaptive Processing (STAP) filter which is well known as an effective way to remove wideband jamming signals. We have chosen Minimum Variance Distortionless Response (MVDR) block-adaptive beamforming algorithm for our development since it can provide relatively fast convergence speed to reach optimal weights, stable and high suppression capability on various types of jamming signals. We will show modeling results for this MVDR type adaptive beamformer and some simulation results. We also show the integrity of the demodulated satellite signals and the accuracy of resulting navigation solutions after anti-jamming operation.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.136-140
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• 2015

In this paper, a comparative studies on the STAP and SFAP were performed, which are known as representative anti-jamming technology for adaptive array antenna. As a method of estimating the weighting vector for simulation, MMSE(Minimum Mean Square Error) algorithm was commonly used and the analyses of the simulation performance in various jamming environments were presented. Especially, performance comparison between STAP and SFAP according to the jamming power J/S(Jamming to Signal Power Ratio), performance comparison in the ratio of jamming bandwidth to signal bandwidth, and performance comparison of BER between STAP and SFAP were presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.745-757
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• 2007

In this paper, we propose a design of GPS anti-jamming system and its operational method, which can effectively suppress interference and jamming signals induced in GPS receiver. The 7-array antenna used in the proposed system is composed of conventional 6 equi-spaced circular elements with one element on the center of antenna and can be efficiently operated under power-constrained conditions. Futhermore, in this paper, we analyze the structure and complexity of STAP and SFAP which are well known techniques in adaptive array antenna signal processing, and we compare the BER performances between STAP and SFAP in various jamming environment based on the same complexity.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.1
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• pp.19-29
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• 2019

This paper analyzes the design factors for GNSS anti-jamming receiver system in which the adaptive beamforming algorithm is applied in GNSS receiver system. The design analysis factors used in this paper are divided into three: antenna, beamforming algorithm, and operation environment. This paper analyzes the above three factors and presents numerical simulation results on antenna and beamforming algorithm.

• ETRI Journal
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• v.41 no.3
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• 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.1
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• pp.30-35
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• 2010

As of today, Frequency Hopping techniques are widely used for over-channel interference and anti-jamming communication systems. In this paper, analysis the performance of robustness on the focus of some general jamming channel. In FH/SS systems, usually SFH(Slow Frequency Hopping) and FFH(Fast Frequency Hopping) are took up on many special communication systems, the SFH, FFH are also combined with a channel diversity algorithm likes NED(Normalized Envelop Detection), EGC(Equal Gain Combines) and Clipped Combines to overcome jammer's attack. This paper propose Adaptive-NED and shows A-NED will be worked well than the others in the some general jamming environments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.419-424
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• 2019

In this paper, we proposed an algorithm to estimate the desired target in wideband jamming signal environment. In order to suppress the jamming signal, we use the spatial time adaptive algorithm and QR decomposition to obtain the optimal weight. The spatial time adaptive algorithm of adaptive array antenna system multiplies the tap delay signal by a complex weight to obtain a weight. In order to minimize the power consumption because of the inverse matrix, optimal weight is obtained by using QR decomposition. Through simulation, we compare and analyze the performance of the proposed algorithm and the existing algorithm. In the target estimation of [-40o,0o,+40o], the proposed algorithm estimated all three targets, but the existing algorithm estimated only [0o] due to of the jamming signal. We prove that the proposed algorithm improves performance by removing the jamming signal and estimating the target accurately.

• 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.