Radar signal cognition based time-frequency transform and high order spectra analysis

Mingqiu, Ren; Tian, Yajie

doi:10.1109/icspcc.2017.8242457

Cited by 6 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A proposal for a radar signal recognition method, this time based on the time-frequency transform and high order spectra analysis, is presented in [ 6 ]. Time-frequency image (TFI) determined using Wigner–Ville Distribution (WVD) is discussed in [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition

Walenczykowska

Kawalec

2022

Sensors

View full text Add to dashboard Cite

This article aims to propose an algorithm for the automatic recognition of selected radar signals. The algorithm can find application in areas such as Electronic Warfare (EW), where automatic recognition of the type of intra-pulse modulation or the type of emitter operation mode can aid the decision-making process. The simulations carried out included the analysis of the classification possibilities of linear frequency modulated pulsed waveform (LFMPW), stepped frequency modulated pulsed waveform (SFMPW), phase coded pulsed waveform (PCPW), rectangular pulsed waveforms (RPW), frequency modulated continuous wave (FMCW), continuous wave (CW), Stepped Frequency Continuous Wave SFCW) and Phase Coded Continuous Waveform (PCCW). The algorithm proposed in this paper is based on the use of continuous wavelet transform (CWT) coefficients and higher-order statistics (HOS) in the feature determination of selected signals. The Principal Component Analysis (PCA) method was used for dimensionality reduction. An artificial neural network was then used as a classifier. Simulation studies took into account the presence of noise interference with signal-to-noise ratio (SNR) in the range from −5 to 10 dB. Finally, the obtained classification efficiency is presented in the form of a confusion matrix. The simulation results show a high recognition test accuracy, above 99% with a signal-to-noise ratio greater than 0 dB. The article also deals with the selection of the type and parameters of the wavelet. The authors also point to the problems encountered during the research and examples of how to solve them.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition

Walenczykowska

Kawalec

2022

Sensors

View full text Add to dashboard Cite

show abstract

Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Tan¹,

Yan²,

Zhang³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter. We propose an emitter identification scheme based on deep learning (DL). The received signal is subjected to time-varying filtered empirical mode decomposition (tvf-EMD). The obtained intrinsic mode functions (IMFs) are subjected to Hilbert transformation to obtain a 3D-Hilbert spectrum, using amplitude frequency aggregation characteristics obtained by projection to express the nonstationary information of the signal and calculate its bispectral diagonal slice as the second feature. A squeeze-and-excitation block (SEB) is introduced to a convolutional neural network (CNN) to focus on the effective area in the feature map, and support vector machine (SVM) is used to fuse the decision information of the two types of features. We collect four types of steady-state signals on a software-defined radio (SDR) sensor platform based on GNU Radio and universal software radio peripherals (USRP), and study the algorithm performance. The results show that our scheme has strong generalization, high recognition accuracy, and broad application prospects compared with other SEI methods.INDEX TERMS Specific emitter identification, empirical mode decomposition (EMD), bispectrum, software-defined radio, channel attention, decision fusion. Preprocess Feature Extraction Receiver DL classifier Signals From Multiple Emitters FIGURE 1. Non-cooperative SEI system based on deep learning.

show abstract

Radar Emitter Recognition Method Based on Bispectrum and Improved Multi-channel Convolutional Neural Network

Yang

Pan

Wang

et al. 2021

2021 6th International Conference on Communication, Image and Signal Processing (CCISP)

View full text Add to dashboard Cite

Radar signal cognition based time-frequency transform and high order spectra analysis

Cited by 6 publications

References 3 publications

Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition

Application of Continuous Wavelet Transform and Artificial Naural Network for Automatic Radar Signal Recognition

Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Radar Emitter Recognition Method Based on Bispectrum and Improved Multi-channel Convolutional Neural Network

Contact Info

Product

Resources

About