Blind source separation in time-varying channels based on adaptive momentum factor

Abstract

The invention provides a blind source separation method under a time-varying channel based on an adaptive momentum factor, belonging to the signal processing field, aiming at ensuring the separation convergence speed while effectively reducing the separation steady-state error, and realizing the following steps: constructing a separation index and obtaining an adaptive update formula of the separation index; obtaining the iterative step length of the separation matrix; acquiring adaptive momentum factor; obtaining the iterative formula of the separation matrix; getting an estimate of the source signal. The invention introduces an adaptive momentum factor in the iteration of the separation matrix, under the condition that the separation convergence speed is not reduced, the steady-state error of the separation is reduced, and the accuracy of the source signal estimation is improved.

Description

technical field [0001] The invention belongs to the field of signal processing, and relates to a blind source separation method under a time-varying channel, in particular to a blind source separation method under a time-varying channel based on an adaptive momentum factor. Background technique [0002] Blind source separation (BSS) is the process of recovering the source signal from the observed signal only according to the statistical independence assumption of the source signal when the source signal and the transmission channel are unknown. According to whether the channel frequency changes with time, the channel is divided into time-invariant channel and time-varying channel, and the BSS can be divided into BSS under the non-time-varying channel and BSS under the time-varying channel. [0003] According to the different ways of data processing, blind source separation algorithm is divided into batch algorithm and online algorithm. Batch processing algorithms such as Fa...

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Problems solved by technology

[0006] The disadvantage of the above method is: the method obtains the step size according to the separation index S in the iteration of the separation matrix, and the value of the step size controls the update range of each element in the separation matrix. After the algorithm converges, each element in the separation matrix Too large or too small element update range will lead to too high steady-state error, because the separation index S is used to update the step size, so that a certain error will inevitably be introduced in the adjustment process of the step size value, making it difficult to reduce the steady-state error. The effect brought by the state error is limited

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