Signal processing method using space coordinates convert realizing signal separation

Abstract

The present invention discloses a signal processing method which realizes signal separation by utilizing space coordinate transformation. The method is used to process multi-channel observation signals which are X1(t)...xm (t) (m is more than 1), namely X(t). Firstly, an arbitrary group of orthogonal base vectors (E) of n-dimensional vector space, namely e1(t)...en(t), is sought, and the n-dimensional vector space is spanned by observation vectors X(t); then, according to the dimension of the vector space, coordinate transformation matrix M in the space is sought; cost function F(M) is built by using the determined transformation matrix M according to the independent characteristics of statistics; the base vectors E is transformed by utilizing the determined transformation matrix M, which determines to have the cost function get extremal transformation matrix Mm; a vector Em is got by utilizing the transformation matrix Mm to execute coordinate transformation to the base vectors; the vector Em and a source signal vector S(t) have the same starting point and are parallel to each other, which is the estimation of the standardized source signal vector S(t).

Description

technical field [0001] The present invention relates to the field of signal processing, and more precisely relates to a processing method for extracting or separating source signals of multi-channel observation signals mixed with multiple statistically independent source signals. Background technique [0002] In a system with multiple observation channels, the source signals that make up the observation signal are often statistically independent, and these source signals contain the desired signal in the measurement. In order to extract or separate the desired signal from the observed signal, separation based on statistically independent features can achieve good results. However, the currently commonly used separation methods are not very ideal for the construction theory of the separation matrix (also known as the unmixing matrix). When the data is updated, it is necessary to obtain a lot of value of the cost function in order to determine the extreme value of the cost fun...

AI Technical Summary

Problems solved by technology

Although the adaptive method can reduce some calculations, once the elements of the mixing matrix change, it takes a long time to stabilize. If the elements of the mixing matrix change frequently (such as in the measurement of arterial blood oxygen saturation The mixing ratio of interference sources in different channels may last for a short time), and the adaptive decomposition method may always obtain a good estimate of the decomposition matrix

Therefore, the currently used separation methods are difficult to achieve real-time and accurate decomposition or extraction of independent source signals, especially in some embedded systems with poor performance, these methods can hardly be realized

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