Kurtosis-based variable-step-size self-adaptive blind source separation method

Abstract

The invention discloses a kurtosis-based variable-step-size self-adaptive blind source separation method, and aims to realize self-adaptation by judging distance between a solution of an algorithm and an optimal solution through kurtosis, adjusting step size on line, and continuously optimizing a separation matrix. The method specifically comprises the following steps of: 1, pre-whitening an observation signal; 2, iterating the separation matrix W by using the whitened signal; and 3, acquiring an optimal matrix to realize source signal separation. The kurtosis-based variable-step-size self-adaptive blind source separation method has the advantages that the step size is controlled according to the change of the kurtosis; the aim of the self-adaptation is fulfilled by judging the distance between the solution of the algorithm and the optimal solution through the kurtosis, adjusting the step size on line, and continuously optimizing the separation matrix; and a contradiction between convergence speed and steady-state errors in a blind source separation process is solved.

Description

technical field [0001] The invention relates to a signal processing method, in particular to a kurtosis-based variable step-length self-adaptive blind source separation method. Background technique [0002] Blind source separation refers to a signal processing method that separates the original signal only through the sensor observation signal in a complex environment where multiple source signals are mixed. It has great application potential in wireless communication, voice, image, seismic signal processing, biomedicine and other fields. Adaptive Blind Separation (EASI), as a typical Least Mean Square (LMS) algorithm, has a fast convergence speed and is a commonly used method for blind source separation. However, this algorithm has a step size optimization problem. The larger the step size, the faster the convergence speed and the larger the steady-state error; on the contrary, the smaller the step size, the slower the convergence speed, but the smaller the steady-state er...

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Problems solved by technology

However, this algorithm has a step size optimization problem. The larger the step size, the faster the convergence speed and the larger the steady-state error; on the contrary, the smaller the step size, the slower the convergence speed, but the smaller the steady-state error.

This contradiction makes the convergence speed and steady-state error of the algorithm unable to satisfy the

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