• 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 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.

• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.1-7
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• 2015

These anti-jamming GPS systems are verified using large anechoic chamber or field-test until now. When using a large anechoic chamber, Independent verification from external enviroments like noise is an advantage but high cost and availability of chamber are disadvantages. And in case of field test, verification under real propagation enviroment is an advantage but security problem of military equipments and problem of making same test condition are disadvantages. This paper presents an newly developed anti-jamming GPS test system. This test system mainly consists of small anechoic chamber, jamming divider, jamming signal generator and satellite simulator. The small anechoic chamber is installed many jamming antennas to transmit multi jamming signals and the jamming divider is newly developed to control multi jamming signals. According to self performance test and combined test with Anti-jamming GPS receiver, we verified our system's reliability.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.2932-2941
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• 1996

Conventional non-regenerative satellite repeaters do not provide anti-jamming capability enough to provide communications between small terminals in jamming environment. In this paper, we analyze the budget and system requirements for Ka-band satellite communication. For the analysis, we modeled and simulated the Ka-band satellite system based on downlink power limited repeater. Assuming that a spread spectrum is applied for anti-jamming purpose, we also analyzed a tolerable jammer-to-signal power ratio under strong up-link jamming in the bit rates of 2.4Kbps and 19.6Kbps.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.32-36
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• 2016

In wireless communication systems, jamming attack is a critical threat. Especially, reactive jamming can jam when the sender and receiver are communicating, which can maximize the attack efficiency of jamming. In this paper, we use the property of multi-input multi-output (MIMO) technology to achieve jamming resilient orthogonal frequency-division multiplexing (OFDM) communications. In particular, we use MIMO interference cancellation to remove the jamming signals strategically. We first investigate the reactive jamming attack model and their impacts on the MIMO-OFDM systems. We then present an iterative channel estimation algorithm that exploits MIMO interference cancellation. Our simulations show various anti-jamming methods and demonstrate the efficiency of our proposed algorithm under the reactive jamming attack.

• 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 the Korea Institute of Military Science and Technology
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• v.18 no.1
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• pp.66-83
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• 2015

Global Positioning System(hereafter; GPS) is recently an essential element in the various navigation and weapon delivery systems of military aircraft. However, GPS is vulnerable to the jamming threats since its signal power is very weak. Therefore, ROK defense has been concerning how to resolve this issue and how to integrate these systems needed, and is trying to acquire the proper anti-jamming GPS system. This study is to provide several schemes against the jamming threats effectively. We propose the several processes to analyze the required capability and demonstrate the result's of modeling and simulations(hereafter; M&S) for this integration of military aircraft, and the mathematical programming model for system optimization of military aircraft anti-jamming GPS system on the basis analysis of M&S results which could be considered available budget and the project characteristic. These schemes will be helpful on proper acquisition of these systems and. We are looking forward to contributing to the integration of anti-jamming GPS system of ROK military aircraft.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.2
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• pp.59-63
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• 2020

The antenna arrays are known to be effective for GPS anti-jamming and the performance can be improved further if a dual-polarized antenna array is used. However, when the Minimum Variance Distortionless Response (MVDR) beamformer is used as a signal processing algorithm for the dual-polarized antenna array, the anti-jamming performance can degrade in the presence of errors in the steering vector that is a key factor of the MVDR beamformer. Therefore, in this paper, the effect of the steering vector error on the anti-jamming performance of the dual-polarized antenna array is analyzed by simulations and the result is compared to that of the single-polarized antenna array.

• 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.