• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2883-2902
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• 2023

Since the existing anti-jamming methods, including intelligent methods, have difficulty against high-speed reactive jamming, we studied a new methodology for jamming utilization instead of avoiding jamming. Different from the existing jamming utilization techniques that harvest energy from the jamming signal as a power supply, our proposed method can take the jamming signal as a favorable factor for frequency detection. Specifically, we design an intelligent differential frequency hopping communication framework (IDFH), which contains two stages of training and communication. We first adopt supervised learning to get the jamming rule during the training stage when the synchronizing sequence is sent. And then, we utilize the jamming rule to improve the frequency detection during the communication stage when the real payload is sent. Simulation results show that the proposed method successfully combated high-speed reactive jamming with different parameters. And the communication performance increases as the power of the jamming signal increase, hence the jamming signal can help users communicate in a low signal-to-noise ratio (SNR) environment.

• 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.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.3
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• pp.229-236
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• 2023

As the importance and dependency of the positioning, navigation, and timing (PNT) information provided by the radio navigation satellite service (RNSS) increases, the vulnerability of RNSS to jamming can lead to significant risks. The signal design under the consideration of anti-jamming performance helps to provide service which is robust to jamming environment. Therefore, it is necessary to evaluate the jamming robustness performance during the design of new signals. In this paper, we introduce figures-of-merit (FoMs) that can be used for an anti-jamming performance analysis of designed signals of interest. We then calculate the FoMs, such as the quality factor (Q factor), tolerable jamming-to-signal ratio (tolerable J/S), and range to jammer (d) for legacy RNSS signals and analyze the results. Finally, based on the results of the analysis, we derive waveform design conditions to obtain good anti-jamming performance. As a result, this paper shows that the waveforms with wide bandwidth leading to good spectral efficiency provide strong anti-jamming performance.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.665-670
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• 2016

This paper proposes a repeat-back jamming signal detection scheme using a correlation ratio test metric of a combined pseudo-random noise signal for global navigation satellite systems. The correlation ratio test metric allows for the monitoring of possible distortions in the signal correlation. The proposed scheme is a modified version of the correlation ratio test metric to detect a repeat-back jamming signal in a multipath environment. Through a Monte-Carlo simulation, it is confirmed that the proposed scheme detects almost the whole case, which is received a repeat-back jamming signal under the 6 dB jamming to signal power ratio.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.483-489
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• 2016

This paper proposes a repeat-back jamming signal detection scheme that utilizes a combined pseudo random noise signal that is effective for processing a global positioning system (GPS) repeat-back jamming signal with the early minus late phase scheme to alleviate any existing multipath signal detection. The proposed scheme uses the combined pseudo random noise signal to treat repeat-back jamming signals like similar multipath signals and can effectively detect a repeat-back jamming signal by applying the early minus late phase scheme to a combined pseudo random noise signal. Through a Monte-Carlo simulation, the detection probability of the proposed scheme is better than the one of the conventional scheme under low jamming to signal power ratio.

• 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.13 no.1
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• pp.133-139
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• 2010

GNSS receiver which uses the weak satellite signal is very vulnerable to the intentional jamming or non-intentional electromagnetic interference. It is a very simple method among the use method of GNSS receiver to vary tracking loop bandwidth of satellite signal appropriately as the jamming signal level. In this paper, this anti-jamming performance is experimented and analyzed in the laboratory and the anechoic chamber by the GNSS simulator to generate the satellite signal and the jamming signal generator to generate the jamming signal.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.10
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• pp.1-9
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• 1990

The performance of noncoherent FH/FSK system in the presence of multitone jamming and noise is analyged. Random and the structured jammings are considered as a multitone jamming model. The probability density function is derived and then optimum decision rule is applied to determine error probability of each cases. As a result, error probabilities of random and structured multitone jamming are shown as a function of number of jamming tones, jamming to signal power ratio, jamming signal phase and one jamming tone power to signal power ratio under Worst-case Jamming interference. It is found that error probability is maximam when one jamming tone power to signal power ratio is 1. Also we know that error performance of random and structured jamming is almost equal.

• 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.