Separation of single-channel mixed signals based on the frequency-division of a convolution-type wavelet packet
Xue Mei, Yuan Xiao-long, Jiashuang Huang, Ma Shilin, Ji WenTian · 2015
Independent component analysis (ICA) is a computational method for separating independent-source signals from a mixed-signal series. The common ICA method cannot be directly used for separating when there is only one sensor collecting the signals. To solve the problem of separating single-channel mixed signals, a novel method based on ICA and the convolutional wavelet packet is proposed to divide a blind source. The method provides a new approach for blind source separation when the number of original signals is limited. First, the sub-band data are obtained by a non-downsampling convolutional wavelet packet. Because the convolutional wavelet packet is not down-sampled, the lengths of different sub-band sequences are the same as the original signal and correspond to a certain frequency band. Second, to decrease the influence of edge frequency aliasing, each sub-band of the wavelet packet is processing using frequency division. Using preprocessing, the authors are able to create a multiple-channel mixed-signal series that can be used to obtain the inputs of the ICA. The method proposed in this paper was applied to blind source separation (BBS) using a simulated sine signal series; real signal series collected from a motor vibration testing platform are also used in testing. The results show that the method produces results in blind source separation that are equivalent to using multiple mixed signals directly.