• 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.

• Journal of Positioning, Navigation, and Timing
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• 제9권2호
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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.

• 한국위성정보통신학회논문지
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• 제10권4호
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• pp.136-140
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• 2015

본 논문에서는 적응형 배열 안테나에 적용되는 대표적 항재밍 기술로 알려진 STAP 및 SFAP 신호처리 방식의 시뮬레이션 성능을 비교 분석하였다. 시뮬레이션을 위해 두 방식의 가중 벡터(weighting vector)를 추정하는 방법으로 공통적으로 MMSE(Minimum Mean Square Error) 알고리즘을 사용하여 다양한 재밍환경에서 시뮬레이션을 통한 성능을 제시하였다. 특히, 재밍 전력 J/S(Jamming to Signal Power Ratio)에 따른 STAP 및 SFAP 성능 비교 분석, 신호대역에 대한 재밍 대역의 비율에 따른 성능 비교 분석 및 두 방식간 BER 성능을 비교하여 재밍 환경에 따른 항재밍 성능을 분석하였다.

• 한국통신학회논문지
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• 제32권8C호
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• pp.745-757
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• 2007

본 논문에서는 GPS 수신기로 유입되는 간섭 및 재밍 신호를 효율적으로 제거 또는 억압하기 위해 배열 안테나(way antenna)를 적용한 항 재밍(anti-jamming) GPS 시스템을 설계하고 운용 방안을 제시하였다. 특히 제안하는 안테나 구조 및 운용 방안은 전통적인 6 원형 배열 안테나(6 circular array antenna) 구조에서 중앙에 1개의 소자를 추가한 7 배열 원형 안테나 구조로써 주어진 간섭 및 재밍 환경 하에서 전력 효율적으로 운용된다. 아울러 적응형 배열 안테나를 사용하여 디지털 신호처리를 수행할 경우 성능이 우수한 것으로 잘 알려진 STAP(Space Time Adaptive Process)와 SFAP(Space Frequency Adaptive Process)를 적용하고 두 방식의 구조 및 복잡도에 관한 분석, 그리고 동일한 복잡도(Complexity) 조건에서 다양한 재밍 환경에서의 BER 성능 비교를 수행하였다.

• Journal of Positioning, Navigation, and Timing
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• 제8권1호
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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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• 제41권3호
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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.

• 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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• 제13권1호
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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.

• 한국정보전자통신기술학회논문지
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• 제12권4호
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• pp.419-424
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• 2019

본 논문에서는 광대역 재밍 신호 환경에서 원하는 목표물을 추정하기 위한 알고리즘을 제안 한다. 재밍 신호를 억제하는 방법으로, 본 연구에서는 시공간적응 알고리즘과 QR분해를 사용하여 최적의 가중치를 획득한다. 시공간적응 알고리즘은 적응배열안테나시스템에서 탭 지연 신호에 복소 가중치를 곱하여 가중치를 생성하고, 역행렬로 인한 전력소모를 최소화하기 위해서 QR분해를 이용하여 최적의 가중치를 획득한다. 모의실험을 통하여, 본 연구에서 제안한 알고리즘과 기존 알고리즘의 성능을 비교 분석한다. [-40o,0o,+40o]의 목표물 추정에서 본 연구에서 제안 한 알고리즘이 3개의 목표물을 모두 추정하였지만 기존 알고리즘은 재밍 신호 때문에 [0o]에서만 추정하였다. 본 연구의 제안 알고리즘이 재밍 신호를 제거하고 원하는 목표물을 정확히 추정하여 성능이 향상되었음을 입증하였다.

• 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.