A Novel Method for VHF Signal Modulation Classification Based on Algorithm of First-Order Cyclic Moment

Abstract

Abstract--In view of the problem of modulation recognition algorithm existing about low recognition rate under environment condition of low SNR, the algorithm of first-order cyclic moment are first presented used in band signal of VHF modulation classification recognition in this paper and the recognition rate increased significantly. The first-order cycle frequencies at which estimated first-order cyclic moment magnitudes exceed the cutoff value were selected as candidate cycle frequencies based on estimation first-order cyclic moment at first-order cycle frequency series, then decided to choose the number of candidate cycle frequency by a cyclostationarity test to realize signal modulation classification recognition, the classification recognition rate can reach more than 98% under condition of SNR is greater than -6 dB.

Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

“…Among these, σ(σ ＞0) is known as the scaling factor, which substitutes (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11) into the unified expression of the Cohen class time-frequency distribution. If the continuous signal is…”

“…However, this method has a low detection probability for signals below −10 dB. The authors of [ 2 ] calculate the first-order cyclic mean of a VHF signal, identify the modulation classification mode, and obtain a high recognition rate of the blind signal modulation mode, but they identify fewer signal types. In [ 3 ], second-order cyclic autocorrelation function and a convolutional neural network are used to compress the signal dimension and reduce the computation amount, and the recognition accuracy is high.…”

A Specific Combination Scheme for Communication Modulation Recognition Based on the Bees Algorithm

“…The current common way of extracting eigenvalues mainly include extracting eigenvalues in the frequency domain and time-domain respectively, analyzing high-order original moment, high-order cumulant, spectral correlation, cyclic spectral correlation, power spectrum, constellation diagram, kurtosis of the signal and other parameters; wavelet transform etc [3][4][5]. In order to improve recognition rate, simultaneous extract multiple eigenvalues and achieved interclass and outside the class recognition by trying different ways, which are introduced in the recent related journal in both domestic and aboard [6][7] and the effect of the work is also impressive. However, how to constantly innovative based on the current ways and improve modulation recognition rate are still challenging research topic.…”