Underdetermined blind source separation (UBSS) method based on maximum matrix diagonal rate

Abstract

The invention discloses an underdetermined blind source separation (UBSS) method based on a maximum matrix diagonal rate. The method comprises the following steps of: constructing inverse matrixes of C*M / N M*M-dimensional sub matrixes of a mixed matrix (wherein M and N are respectively the number of sensors and the number of source signals); multiplying the inverse matrixes by observation signal vectors to acquire initial estimation signal vectors; and sequentially calculating the covariance matrix, the solid matrix, the absolute value matrix and the diagonal rate of each initial estimation signal vector, selecting the initial estimation signal vector corresponding to the maximum diagonal rate as estimation of a source signal vector, and thus realizing underdetermined separation of sourcesignals. By the method, the requirement for source signal sparseness is reduced, aliasing of road source signals is allowed at each time frequency point at most, and the underdetermined separation problem of music signals and noise signals is solved. The requirement for the statistical property of the source signals is low, and the underdetermined separation problem of Gaussian signals and related signals is solved. In addition, by the method, processing of each time frequency point and each sub matrix can be executed in parallel, and hardware implementation is facilitated.

Description

technical field [0001] The invention relates to a method for blindly separating instantaneous mixed signals under the condition of underdetermination. The method can separate sparse, weakly sparse or correlated signals, and can be applied in the fields of signal processing, biomedicine and communication. Background technique [0002] Blind Source Separation (BSS) is a technology that determines a transformation based on the observed mixed data vector to restore the original signal or source. Typically, the observed data vector is the output of a set of sensors, where each sensor receives a different combination of source signals. The term "blind" has two meanings: a. the source signal cannot be observed; b. how the source signal mixes is unknown. When the number of source signals is more than the number of observation signals, it is an underdetermined blind separation (UBSS) problem, which is closer to practical applications, and it is also a technical difficulty of linear...

AI Technical Summary

Problems solved by technology

The advantage of this method is that the traditional overdetermined separation technology can be used. The disadvantage is that the separation effect depends on the quality of the newly constructed observation signal. Once the signal is not well constructed, the separation quality will deteriorate.

The two-step method is actually an extension of the basis tracking method. It obtains the optimal solution by solving the linear equation and constraining the solution. Using the sparsity of the signal, it minimizes the 0 norm and then constrains the solution. The 0 norm is very easy to deal with. Inconvenient and especially sensitive to noise

In 1999, D.L.Donoho demonstrated the equivalence of using the smallest norm of 1 and the smallest norm of 0 under certain conditions. The norm of 1 is easier to deal with than the norm of 0. The optimal solution can be easily obtained by using linear programming, and the anti-noise performance Although it is better than the 0-norm criterion, the effect is still unsatisfactory. In addition, the algorithm is based on the sparsity of the signal in the time domain, so the general separation effect is very poor.

The time-frequency masking method was first proposed by Sam T.Roweis in 2000. In 2004, Yilmaz and Rickard combined the DUET algorithm to further develop the time-frequency masking algorithm. However, this type of method strictly requires that the source signal approximately satisfies W-dislocation orthogonality, harsh conditions

In 2005, Abrard proposed the TIFROM algorithm. This method requires that only one source signal exists in the observation signals of several adjacent time-frequency windows. The length of the time-frequency window is difficult to determine, and it is impossible to search for all single-source time-frequency frequency domain

In 2011, Zhou Guoxu and others proposed a new method - nonlinear projection and column masking (NPCM) to estimate the mixing matrix when the signal is not strictly sparse; Lu Fengbo et al. based on the matrix diagonal realizes underdetermined blind source separation under weak sparse conditions, but this method can only deal with uncorrelated signals

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