Identification of phase shift keying-like signals based on phase statistical features
In noncollaborative communication,phase-shift keying(PSK)class signals are difficult to classify and recognize in a complex environment owing to phase similarity.To solve this problem,this study presents a method for recovering the characteristics of signal phase information,which are obtained by Gaussian kernel density estima-tion based on the derivation of the probability density of signal phase information under Gaussian noise.In addi-tion,the method is generalized from additive Gaussian noise to additive alpha steady-state distribution noise through simulation experiments.Under this method,the proposed features can be accurately recognized for five types of sig-nals,namely,BPSK,QPSK,OQPSK,π/4_DQPSK,and 8 PSK,under alpha noise.It is shown that using the support vector machine method,the overall recognition rate of the signals can achieve more than 90%at signal-to-noise ratios above 0 dB.
noncollaborative communicationGaussian kernel density estimationphase information featurealpha steady-state distribution noisephase-shift keying(PSK)modulation identificationsignal-to-noisesupport vec-tor machin(SVM)method
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