JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

Qu, Qizhe; Wei, Shunjun; Liu, Shan; Liang, Jiadian; Shi, Jun

doi:10.1109/tvt.2020.3032197

Cited by 80 publications

(19 citation statements)

References 22 publications

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“…These networks are effectively utilised in the field of jamming countermeasures. Qu proposed a jamming recognition network based on robust power-spectrum features [38]. Fu extracted features in time domain, frequency domain and fractal dimensions as sorting parameters.…”

Section: Introductionmentioning

confidence: 99%

An anti‐jamming method in multistatic radar system based on convolutional neural network

Liu

Gong

Zhang

et al. 2021

IET Signal Processing

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For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple-radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti-deception jamming, which takes full advantage of unknown information of echo data to obtain multi-dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. The simulation results show that the proposed method can extract the multidimensional and separable essential features of echoes, and all these features have a strong degree of differentiation between targets and jamming, which effectively reduce the influence of noise and pulse number. At the same time, the influence of radar distribution on jamming discrimination under non-ideal conditions is relieved, when the correlation coefficient of the true target reaches 0.4, the discrimination probability remains above 85%, which broadens the boundary conditions of the application process.

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Section: Introductionmentioning

confidence: 99%

An anti‐jamming method in multistatic radar system based on convolutional neural network

Liu

Gong

Zhang

et al. 2021

IET Signal Processing

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“…Modern warfares are information and electronic warfare. Many enemies and our radars are deployed on the battlefield, coupled with natural electromagnetic radiation and man-made electromagnetic radiation interference, making the electromagnetic environment of the battlefield more complicated [1]. e UAV LiDAR detection system plays an important role in informatization electronic warfare operations.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

et al. 2021

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With the rapid development of science and technology, UAVs (Unmanned Aerial Vehicles) have become a new type of weapon in the informatization battlefield by their advantages of low loss and zero casualty rate. In recent years, UAV navigation electromagnetic decoy and electromagnetic interference crashes have activated widespread international attention. The UAV LiDAR detection system is susceptible to electromagnetic interference in a complex electromagnetic environment, which results in inaccurate detection and causes the mission to fail. Therefore, it is very necessary to predict the effects of the electromagnetic environment. Traditional electromagnetic environment effect prediction methods mostly use a single model of mathematical model and machine learning, but the traditional prediction method has poor processing nonlinear ability and weak generalization ability. Therefore, this paper uses the Stacking fusion model algorithm in machine learning to study the electromagnetic environment effect prediction. This paper proposes a Stacking fusion model based on machine learning to predict electromagnetic environment effects. The method consists of Extreme Gradient Boosting algorithm (XGB), Gradient Boosting Decision Tree algorithm (GBDT), K Nearest Neighbor algorithm (KNN), and Decision Tree algorithm (DT). Experimental results show that, comprising with the other seven machine learning algorithms, the Stacking fusion model has a better classification prediction accuracy of 0.9762, a lower Hamming code distance of 0.0336, and a higher Kappa coefficient of 0.955. The fusion model proposed in this paper has a better predictive effect on electromagnetic environment effects and is of great significance for improving the accuracy and safety of UAV LiDAR detection systems under the complex electromagnetic environment on the battlefield.

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“…Jamming recognition techniques are primarily studied in the context of radar [6]- [9], focusing on the jammer types detection based on the jamming signals. The work in [6] and [7] study the jamming signal type recognition using convolution neural networks (CNN) while the authors in [8] employ CNN to classify the jamming signal based on fast Fourier transform (FFT).…”

Section: Introductionmentioning

confidence: 99%

“…The work in [6] and [7] study the jamming signal type recognition using convolution neural networks (CNN) while the authors in [8] employ CNN to classify the jamming signal based on fast Fourier transform (FFT). The authors in [9] study power spectral of the jamming signals to recognize the jamming type. The proposed techniques in [6]- [9] need a high sampling rate with high accuracy of the signal detection.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [9] study power spectral of the jamming signals to recognize the jamming type. The proposed techniques in [6]- [9] need a high sampling rate with high accuracy of the signal detection. As a result, legitimate users are required to detect the jamming signals with high accuracy, which is not possible in all scenarios, especially in cases where the jammers jam channels with low jamming power.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach

Pourranjbar¹,

Kaddoum²,

Saad³

2021

Preprint

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With the advent of intelligent jammers, jamming attacks have become a more severe threat to the performance of wireless systems. An intelligent jammer is able to change its policy to minimize the probability of being traced by legitimate nodes. Thus, an anti-jamming mechanism capable of constantly adjusting to the jamming policy is required to combat such a jammer. Remarkably, existing anti-jamming methods are not applicable here because they mainly focus on mitigating jamming attacks with an invariant jamming policy, and they rarely consider an intelligent jammer as an adversary. Therefore, in this paper, to employ a jamming type recognition technique working alongside an anti-jamming technique is proposed. The proposed recognition method employs a recurrent neural network that takes the jammer's occupied channels as inputs and outputs the jammer type. Under this scheme, the real-time jammer policy is first identified, and, then, the most appropriate countermeasure is chosen. Consequently, any changes to the jammer policy can be instantly detected with the proposed recognition technique allowing for a rapid switch to a new anti-jamming method fitted to the new jamming policy. To evaluate the performance of the proposed recognition method, the accuracy of the detection is derived as a function of the jammer policy switching time. Simulation results show the detection accuracy for all the considered users' numbers is greater than 70% when the jammer switches its policy every 5 time slots and the accuracy raises to 90% when the jammer's policy switching time is 45.

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JRNet: Jamming Recognition Networks for Radar Compound Suppression Jamming Signals

Cited by 80 publications

References 22 publications

An anti‐jamming method in multistatic radar system based on convolutional neural network

An anti‐jamming method in multistatic radar system based on convolutional neural network

A Prediction Method of Electromagnetic Environment Effects for UAV LiDAR Detection System

Jamming Pattern Recognition over Multi-Channel Networks: A Deep Learning Approach

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