Source Signal Recovery Method Based on Gradient Descent and Newton's Method for Underdetermined Blind Source Separation

Abstract

The invention discloses a gradient descent method and Newton method based underdetermined blind source separation source signal recovery method. The method comprises the steps of firstly, obtaining an observation signal matrix; secondly, clustering all column vectors in the observation signal matrix to obtain a hybrid matrix; thirdly, calculating to-be-recovered source signal column vectors according to the observation signal matrix and the hybrid matrix; and finally, updating the to-be-recovered source signal column vectors by utilizing a gradient descent method, a Newton method and a projection method to obtain a recovered source signal. According to the method, the defects that the source signal recovery precision is easily influenced by a noise error and the computing complexity is high in the prior art are overcome, so that the method has the advantage that the source signal can be quickly recovered while relatively high recovery precision is kept.

Description

technical field [0001] The invention belongs to the technical field of communication, and further relates to an underdetermined blind source separation source signal restoration method based on a gradient descent method and a Newton method in the technical field of signal processing. The invention can process military communication signals, image signals and biomedical signals, and realize the recovery of underdetermined blind source separation source signals under the condition that the mixing matrix has been estimated. Background technique [0002] Underdetermined blind source separation is to estimate the source signal only by using the observed signal when the parameters of the transmission channel are unknown and the number of observed signals is less than the number of source signals. The underdetermined blind source separation technology only needs a small number of sensors to receive mixed signals, which not only meets specific occasions, but also saves costs. [00...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this patented technology is that, according to the observed signal matrix and the estimated mixed matrix, the linear programming method is used to recover the source signal, and the recovery accuracy of the source signal is easily affected by errors, which is difficult in practical applications. Guaranteed recovery of the source signal with high accuracy

However, the disadvantage still exists in this patent is that, using a fixed iteration step size, it is difficult to balance the source signal recovery accuracy and time complexity, and it is difficult to guarantee the source signal recovery at a faster speed in practical applications.

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