Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise

Xu, Weichao; Lai, Huadong; Dai, Jisheng; Zhou, Yanzhou

doi:10.1016/j.dsp.2022.103443

Cited by 9 publications

(2 citation statements)

References 59 publications

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“…The signals obtained by electronic reconnaissance are usually mixed signals, of various radiation sources and environmental noise in the receiver frequency band, and radar pulses will be offset and lost to some extent. Due to the limitations of the receiver’s bandwidth and sensitivity, under the limitation of the fixed false alarm rate in the signal detection link, with the decrease in the signal-to-noise ratio, the radar will generate more lost pulses [ 38 ]. The degree of lost pulse is affected by receiver performance and the false alarm rate setting.…”

Section: Problem Formulationmentioning

confidence: 99%

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Pan

Xiong

Zhuo

2023

Sensors

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Mode recognition is a basic task to interpret the behavior of multi-functional radar. The existing methods need to train complex and huge neural networks to improve the recognition ability, and it is difficult to deal with the mismatch between the training set and the test set. In this paper, a learning framework based on residual neural network (ResNet) and support vector machine (SVM) is designed, to solve the problem of mode recognition for non-specific radar, called multi-source joint recognition framework (MSJR). The key idea of the framework is to embed the prior knowledge of radar mode into the machine learning model, and combine the manual intervention and automatic extraction of features. The model can purposefully learn the feature representation of the signal on the working mode, which weakens the impact brought by the mismatch between training and test data. In order to solve the problem of difficult recognition under signal defect conditions, a two-stage cascade training method is designed, to give full play to the data representation ability of ResNet and the high-dimensional feature classification ability of SVM. Experiments show that the average recognition rate of the proposed model, with embedded radar knowledge, is improved by 33.7% compared with the purely data-driven model. Compared with other similar state-of-the-art reported models, such as AlexNet, VGGNet, LeNet, ResNet, and ConvNet, the recognition rate is increased by 12%. Under the condition of 0–35% leaky pulses in the independent test set, MSJR still has a recognition rate of more than 90%, which also proves its effectiveness and robustness in the recognition of unknown signals with similar semantic characteristics.

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Section: Problem Formulationmentioning

confidence: 99%

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Pan

Xiong

Zhuo

2023

Sensors

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“…Currently, people tend to pay more attention to the optical features and echo characteristics of the target, ignoring the passive microwave signal [1,2]. However, it should be noted that compared to optical and echo features, passive radar signals have three advantages [3][4][5]: (1) Signal reception is passive and has stronger stealth characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Xiong,

Pan,

2023

Remote Sensing

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Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

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A Simple Chirping‐Based Spectrum Sensing Scheme for Cognitive Radio Applications

Taki,

Tanguy,

Mansour

2024

Int J Communication

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In this study, we propose a simple spectrum sensing method based on exploiting the properties of a group‐delay phaser. Following the theory that an additive white noise should have a flat spectrum over the band of interest, which is not the case for most data‐modulated signals, the spectrum shape of input waveforms has been the test variable. The latter enables a clear distinguishing method between a noise background and a communication signal of a transmission body operating over the desired band. The phaser scatters the frequency components of received signals in time space, allowing a time‐domain inspection of the corresponding spectral response. The accurate closed‐form analytical expression for the probability of detection in an additive white Gaussian noise (AWGN) channel has been derived, in addition to the false alarm probability. The probability of detection has been studied versus signal‐to‐noise ratio (SNR) in AWGN and multipath channels. As well, the receiver operating characteristic (ROC) curves have been plotted for different values of SNR. A good performance has been achieved by our scheme, which has recorded a detection probability of 0.86 for a false alarm probability of 0.1, and further shown a kind of robustness against noise uncertainties. Experimental works have eventually been conducted, and the empirical results also validate the effectiveness of the elaborated approach. An improvement of around 30% in the probability of detection has been realized over the energy‐based sensing technique, at low measures of false alarm probability.

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Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise

Cited by 9 publications

References 59 publications

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

Working Mode Recognition of Non-Specific Radar Based on ResNet-SVM Learning Framework

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

A Simple Chirping‐Based Spectrum Sensing Scheme for Cognitive Radio Applications

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