Signal separation method based on particle swarm optimization

Abstract

The invention discloses a signal separation method based on particle swarm optimization. The method includes the steps: (1) inputting observation signals; (2) building an independent component analysis algorithm optimization model by taking a difference value between simple products of a minimum separated signal joint probability and a marginal probability as an optimization target; (3) estimating the number of source signals according to a singular value decomposition method and determining the number of optimization variables according to the number of the source signals; (4) calculating correlation coefficients of the observation signals, and determining the value range of the optimization variables; (5) optimizing separation matrixes by the aid of a particle swarm optimization algorithm; (6) taking particles with optimal fitness in the last generation population after optimization as an optimal separation matrix and multiplying the optimal separation matrix with the mixed signals to obtain the optimal separation signals. The method based on independent component analysis of the particle swarm optimization has universal applicability and effectively solves various blind source separation problems.

Description

Technical field [0001] The invention belongs to the technical field of signal processing, and relates to a signal separation method, especially a signal separation method based on particle swarm optimization. Background technique [0002] In many signal processing applications, the observation signal collected by the sensor is often a mixed signal formed by multiple sources, and the signal quality is poor. In order to reduce noise interference and improve signal quality, independent component analysis (ICA) can be used to process the observed signal and recover the independent source signal. [0003] In the prior art, many algorithms have emerged to solve the ICA problem. The most classic algorithm is the FastICA algorithm, which has the advantage of fast convergence speed. Compared with gradient-based algorithms, there is no need to choose a step size. The literature uses FastICA algorithm when solving the BSS problem. The Infomax algorithm proposed by Bell and Sejnowski in 1995...

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Problems solved by technology

[0004] The above methods in the prior art have a good application effect on conventional blind signal separation problems, but for unconventional problems such as super-Gaussian source mixed with sub-Gaussian source, strong source signal mixed with weak source signal, etc., the application effect is not ideal: for super-Gaussian source In the case of mixed sub-Gaussian sources, the three ICA algorithms cannot achieve accurate separation; for the case of strong signals mixed with weak signals, the FastICA algorithm is completely invalid, and the extended Infomax algorithm and JADE algorithm perform better, but there are still some differences between the details and the source signal. deviation

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