Underdetermined blind source separation method based on density

Abstract

The invention discloses an underdetermined blind source separation method based on density, mainly solving the problems that the computation complexity of the prior art is high, the prior art is easily influenced by an initial value and the number of source signals needs to be given. The method comprises the following steps: after the low energy sampling data of an observed signal is removed, projecting the observed signal onto a right half unit hypersphere; computing the density parameters of all projective points, and deleting the projective points with smaller density; utilizing an improved K-mean value clustering algorithm to cluster the surplus projective points and determining the optimal clustering number and clustering center; removing the cluster with less number of data objects, wherein the number of the surplus clusters is the estimated value of the number of source signals, and the corresponding clustering center is the estimated value of each column vector of a confusion matrix; and according to the observed signal and the estimated confusion matrix, adopting a linear programming method to recover the source signals. According to the method, the computation complexity is reduced, the influence of the initial value on the estimated performance is reduced, the confusion matrix can be estimated when the number of the source signals is unknown, and the estimating precision of the confusion matrix and the source signals can be increased.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to an underdetermined blind source separation method, which can be used in the fields of mechanical failure detection, voice signal processing, image signal processing, biomedical engineering and the like. Background technique [0002] Blind Sources Separation (BSS) technology is a new type of signal processing technology developed in the 1990s. It is widely used in mechanical fault detection, voice signal processing, image signal processing, biomedical engineering and other fields. Applications. Blind source separation refers to recovering the source signal only from the observed signal when the source signal and transmission channel parameters are unknown. Underdetermined Blind Source Separation (Undetermined BSS) requires that the number of observation signals is smaller than the number of source signals, which is more realistic and more challenging. The linear instantaneous mi...

AI Technical Summary

Problems solved by technology

The Laplace potential function method suitable for high-dimensional space has strong anti-noise ability, but in order to ensure the estimation of each local maximum, this method selects all data objects as the initial cluster center, resulting in algorithm complexity high, impractical

The robust competitive clustering method reduces the complexity of the algorithm, but its robustness is poor, and the accuracy of the mixture matrix estimation is low

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