Convolutive blind signal separation method based on multi-target optimization joint block diagonalization

Abstract

The invention discloses a convolutive blind signal separation method based on multi-target optimization joint block diagonalization, mainly solving the problem that in the prior art, not all source signals can be accurately separated from convolutive aliasing signals. The method comprises the steps of: (1) obtaining observation data; (2) calculating the second-order delay correlation matrix of the observation data; (3) constructing a block diagonalization matrix and dividing into submatrixes; (4) establishing a multi-target optimization model about the block diagonalization matrix; (5) estimating the block diagonalization matrix in dependence on the multi-target optimization model; (6) determining whether the difference absolute value between twice block diagonalization matrix evaluated errors is larger than an iteration termination threshold, and if yes, outputting the block diagonalization matrix, and if no, returning to the step (5); and (7) utilizing the block diagonalization matrix to separate source signals from observation signals. The method can accurately separate all source signals from convolutive aliasing signals, has the characteristics of low complexity and high separation efficiency, and can be used for processing voice signals and communication signals.

Description

technical field [0001] The invention relates to the technical field of signal processing, and further relates to a convolution blind signal separation method, which can be used to realize the processing of voice signals and communication signals. Background technique [0002] Blind signal processing has a wide range of applications in various fields such as wireless communication, radar and sonar, image restoration, and speech enhancement. Blind signal separation is an important part of blind signal processing. Its only assumption is that the source signals are statistically independent from each other, which is approximately true for most situations in nature, and the blind signal separation technique does not require any And the prior information of the transmission channel makes this technique a very practical signal processing method. Especially when it is difficult or impossible to model the transmission channel from multiple sources to the sensor, blind signal separat...

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Problems solved by technology

Although this method uses the construction cost function to solve the three groups of matrix factors, which reduces the computational complexity and overcomes the shortcomings of being sensitive to noise and prone to singular solutions, but still has the disadvantage that this method is a quadratic optimization method. Without any constraints on the block diagonalization matrix, it is easy to get a degenerate solution, resulting in the inability to achieve separation of all source signals

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