Automatic modulation classification of frequency hopping signals based on decision-level fusion model in multipath channels

Abstract

In many complex wireless communication scenarios, frequency hopping technology has attracted widespread attention due to its strong anti-fading and anti-interference characteristics. In this paper, we propose an improved support vector machine (SVM) classifier based on the decision-level fusion (DLF) model, referred to as DLF-SVM, for automatic modulation classification (AMC) of frequency hopping signals in multipath channels. To estimate parameters and get the sliced samples, the frequency hopping pattern is first obtained by performing the time-frequency analysis on the received signal. Then, for samples after being preprocessed, we extract the correlation coefficients of the cyclic spectral section as categorical features. Finally, focusing on the idea of the DLF model, an improved SVM classifier based on the prior probability weight matrix is constructed to realize the AMC of frequency hopping signals. Simulation results show that our proposed algorithm can significantly improve the classification accuracy of binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and minimum shift keying (MSK) signals under low signal-to-noise ratio (SNR) and multipath channels.