Abstract
In order to solve the problems that the accuracy of modulation recognition algorithms of MSK and MQAM signals is not ideal under the condition of low signal-to-noise ratio (SNR) in Additional White Gaussian Noise (AWGN) environment, two novel features, the differential nonlinear phase Peak Factor (PF) and the reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing, are constructed, which can complete the recognition of MSK signal and the classification of MPSK and MQAM signals respectively. This paper proposes a mixed recognition algorithm based on the two new features and other classical features, and design a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM) to recognize eleven kinds of digital modulation signals. The simulation results illustrate that the algorithm can achieve the classification of the modulation signals {2FSK, MSK, 2ASK, 4ASK, 8ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM} with small SNR. When the SNR is not less than -1dB, the recognition rate of the classifier for all signals exceed 97%, which validates the effectiveness of the proposed modulation recognition algorithm.
Highlights
The automatic modulation classification (AMC) technology of communication signal is widely used in modern military war and civil electromagnetic supervision, which provides technical basis and guarantee for the realization of intelligent signal reception and processing
In order to realize the multiclassification of eleven kinds of digital modulated signals, this paper proposes a tree shaped multi-layer smooth support vector machine classifier based on feature selection algorithm (FS_DT-SSVM)
In this paper, in order to solve two kind complex problems of communication signals modulation classification including MSK extracted from 2FSK and inter-classification of MPSK and MQAM, differential nonlinear phase Peak Factor (PF) and reciprocal of amplitude envelope variance of cyclic spectrum at zero frequency cross section after difference and forth power processing (Y2) were proposed respectively
Summary
The automatic modulation classification (AMC) technology of communication signal is widely used in modern military war and civil electromagnetic supervision, which provides technical basis and guarantee for the realization of intelligent signal reception and processing. Authors combine the information entropy features of the signal with the feature selection algorithms and use five different ensemble learning classifiers to complete the classification of a variety of digital modulation signals [4]. The new features that can complete recognition of MSK signal and classification of MPSK and MQAM signals are rarely proposed, and this paper will study the above two problems and realize the extraction of the two new features. Other researchers introduce gray-scale algorithm into the training process of SVM and combines the feature parameters extracted by K-means clustering algorithm to complete the modulation pattern classification of MPSK and MQAM signals [11].
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