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文中利用了MPSK信号经Morlet小波变换后的相位特性,提出了一种识别MPSK信号的方法。Morlet小波有良好的时频局部特性,与Hilbert变换相比,MPSK信号通过Morlet小波变换后获得了信噪比增益,因此可以更精确提取信号的瞬时相位。受加性高斯白噪声污染的MPSK信号经Morlet小波变换后,相位的概率密度函数可以用Tikhonov函数近似,由近似函数利用相位的统计性质识别MPSK信号。以已有的一种识别算法为例识别MPSK信号,仿真结果表明:在正确识别率为0.9时,采用Morlet小波变换,所需信噪比低约3dB。
In this paper, the phase characteristics of MPSK signals after Morlet wavelet transform are used, and a method of identifying MPSK signals is proposed. Compared with Hilbert transform, Morlet wavelet has good time-frequency local characteristic. Compared with Hilbert transform, MPSK signal obtains signal-to-noise ratio gain by Morlet wavelet transform, so the instantaneous phase of signal can be extracted more accurately. The Morpheme wavelet transform of MPSK signals contaminated by additive white Gaussian noise can approximate the probability density function of the phase with the Tikhonov function, and use the approximation function to identify the MPSK signal using the statistical properties of the phases. An existing recognition algorithm is used as an example to identify MPSK signals. Simulation results show that when the correct recognition rate is 0.9, the Morlet wavelet transform is used, and the required SNR is about 3 dB lower.