Deep representation method for radar emitter signal using wavelet packets decomposition

Cao, Yang; Bai, Jin Liang; Li, Hongbo; Zhao, Jinsong; Liu, Shuo; Gao, Zhiyong

doi:10.1049/joe.2019.0256

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…Wavelet packet decomposition is an extension of wavelet transform, which can decompose signals at high frequency and low frequency at the same time, and effectively reflect the difference of information contained in different signals. 17,18 It is also known as the optimal subband tree structure, which can realize the hierarchical representation of signal features and obtain the approximate and detailed features of signals. 19 The basis function of wavelet packet decomposition is not unique, and different wavelet basis functions can correspond to different applications.…”

Section: Methodsmentioning

confidence: 99%

Improvement on the Identification and Discrimination Ability for Rice of Electronic Tongue Multi-Sensor Array Based on Information Entropy

Lü

Fang

et al. 2022

J. Electrochem. Soc.

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An approach for improving the identification and discrimination ability of an electronic tongue multi-sensor array was developed. The detail signal was obtained by decomposing the original voltammetric signal using wavelet packet decomposition, and the feature value was extracted by fast Fourier transform with the influence of collinearity eliminated. Based on the principle of information entropy, the sensing entropy of a single electrode and between electrodes in the multi-sensor array were defined, and the unit sensing vector and interactive sensing vector were constructed. The results showed that six unit sensing entropies could be effectively used for the identification of rice origin, and all interactive sensing vectors for the discrimination of rice type. SVM and KNN classifiers were employed. The results showed that the training and prediction accuracy of SVM with interactive sensing vector as the input for identifying rice origin were 89.0% and 82.9%, respectively, and that for distinguishing rice type were 96.0% and 88.6%, respectively. In conclusion, the SVM model with interactive sensing vector could be an approach to accurately identify rice origin and type. The identification and discrimination ability of multi-sensor array could be enhanced by using the sensing interaction information based on information entropy.

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Section: Methodsmentioning

confidence: 99%

Improvement on the Identification and Discrimination Ability for Rice of Electronic Tongue Multi-Sensor Array Based on Information Entropy

Lü

Fang

et al. 2022

J. Electrochem. Soc.

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“…Approaches leveraging time-frequency distribution features [9], entropy values [10], resemblance coefficients [11], and wavelet packet features [12] have shown promising results. Nevertheless, most existing algorithms face challenges in accurately identifying signals when the SNR is below 5 dB [13][14][15]. Only a limited number of algorithms have demonstrated satisfactory performance within the SNR range of 0 dB to 5 dB [16,17], while the majority of methods completely fail to function when the SNR drops below 0 dB.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Liu

Song

Chen

et al. 2023

Drones

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In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs.

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“…Some scholars have also proposed cyclostationary method, wavelet packet method, visual graph method and so on. For some intra-pulse modulation, they have good resolution effect, but there are still problems of high complexity, which need to be improved in practical engineering application [10][11][12]. However, the resemblance coefficient method is favored by some scholars because it directly analyzes the correlation according to the signal spectrum, has low computational complexity, and still has high resolution for different modulation types of signals under the condition of low signal-to-noise ratio [13][14].…”

Section: Introductionmentioning

confidence: 99%

Radar Emitter Identification Based on Spectral Resemblance Coefficient

Wang¹,

Zhang²,

Zeng³

et al. 2022

J. Phys.: Conf. Ser.

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The traditional radar emitter identification method based on the five parameters of the pulse is difficult to achieve the desired effect in today’s abnormally dense electromagnetic environment. Modern radars often impose intentional modulation in the signal to improve the anti-jamming and low interception performance. Therefore, a radar emitter identification method based on the spectrum resemblance coefficient is proposed in this paper, which reflects the approximation degree between the signal spectrum and various basis functions by calculating the resemblance coefficients between them. Simulation results show that the method proposed in this paper has strong anti-interference capability and can effectively distinguish different types of intra-pulse modulated radar signals.

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Deep representation method for radar emitter signal using wavelet packets decomposition

Cited by 5 publications

References 5 publications

Improvement on the Identification and Discrimination Ability for Rice of Electronic Tongue Multi-Sensor Array Based on Information Entropy

Improvement on the Identification and Discrimination Ability for Rice of Electronic Tongue Multi-Sensor Array Based on Information Entropy

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Radar Emitter Identification Based on Spectral Resemblance Coefficient

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