Signal processing method and device

Abstract

The embodiment of the invention relates to a signal processing method and a device. The method includes the following steps: in a period of time, receiving a mixed signal and obtaining the amplitude information of the received mixed signal; according to the third-order cumulant and the fourth-order cumulant of the amplitude information of the mixed signal, obtaining objective function through calculation; and using the objective function to obtain an independent signal in the mixed signal. The method can cause ICA algorithm to be applicable to unmixing treatment of various signals. Another method includes the following steps: using Newton iteration formula with a regulatory factor that can cause the objective function to descend according to given norm to conduct iterative treatment on initial weight vector until the iterated weight vector is converged, and obtaining the converged weight vector; receiving the mixed signal in a period of time and acquiring the amplitude information of the received mixed signal; and according to the converged weight vector, conducting unmixing treatment on the mixed signal and acquiring the independent signal in the mixed signal. The method has little restraint in selection of the initial value of the weight vector in partitioning matrix and high operation efficiency.

Description

technical field [0001] The present invention relates to a signal processing method and device, in particular to a signal processing method and device based on independent component analysis (Independent Component Analysis, ICA for short). Background technique [0002] ICA is a multi-channel signal processing method developed by signal separation technology in recent years. It is a signal processing method for extracting independent sources of signals when there are only observation data and the aliasing method of signal sources is unknown. Blind signal separation via ICA has potential advantages in speech recognition, communication, biomedical signal processing, image processing. [0003] For any ICA algorithm, it can be summed up as: ICA algorithm = objective function + optimization algorithm. Suppose n random variables x are observed 1 , x 2 ,...x n , and these variables are composed of another n random variables s 1 ,s 2 ,...s n obtained by linear combination, that...

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Problems solved by technology

[0004] In the process of realizing the present invention, the inventors have found that there are at least the following problems in the prior art: the existing ICA method can target sources such as sub-Gaussian signals, super-Gaussian signals, and signals with symmetric or asymmetric probability density functions. Signals are separated, but these signals cannot have good separation performance, which leads to the limitation of the applicable signal range of the existing ICA algorithm

Moreover, the existing ICA method is sensitive to the selection of the initial value of the weight vector in the separation matrix, and has poor convergence performance and low efficiency.

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