NPCA-based post nonlinear blind source separation method

A blind source separation, non-linear technology, applied in the field of signal processing, can solve problems such as lack of constraints on the value range of the solution to be optimized, unsuitable separation of signals, and impact on stability.

Inactive Publication Date: 2015-02-04
JIMEI UNIV
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Problems solved by technology

Although these algorithms use intelligent swarm optimization algorithms with strong optimization capabilities to optimize the objective function, they lack constraints on the range of values ​​to be optimized, which affects the stability of their solutions; in addition, they are not suitable for online separation signals

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  • NPCA-based post nonlinear blind source separation method
  • NPCA-based post nonlinear blind source separation method
  • NPCA-based post nonlinear blind source separation method

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Embodiment Construction

[0065] The present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0066] As a specific example, such as Figure 1 to Figure 3 Shown, a kind of post-nonlinear blind source separation method based on NPCA of the present invention comprises the following steps:

[0067] Step 1: Load the observed signals of T sampling points

[0068] T is the total number of sampling points of the observed signal, and t is the number of sampling points.

[0069] The specific implementation process of the step 1 is: n mutually independent zero-mean unknown source signals s(t)=[s 1 (t),s 2 (t),...,s n (t)] T , through an unknown instantaneous linear mixed system m linearly aliased signals after mixing Then through the reversible nonlinear function F(·)=[f 1 (·), f 2 (·),…, f m (·)] T Observable signal x(t) = [x 1 (t),x 2 (t),...,x m (t)] T , its formula is as follows:

[0070] ...

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Abstract

The invention relates to the technical field of signal processing, and particularly relates to a NPCA-based post nonlinear blind source separation method. The NPCA-based post nonlinear blind source separation method of the invention comprises the following steps: step 1, observation signals of T sampling points are loaded; step 2, W^(t) is initialized, and elements thereof are randomly generated within [-11]; step 3, whether or not t<=T is judged, step 4 is executed if t<=T, or step 8 is executed; step 4, y(t) optimal estimation on the source signals is carried out, namely, the generalized inverse matrix W^(t) of an optimal time-varying system A^(t) is solved; step 5, column vectors of W^(t) are stacked to form a column vector with N<2> dimensions, W<rightward arrow>(t) with n<2>*n<2> dimensions is constructed, and a nonlinear function suitable for source signal separation is selected; step 7, the optimal W<rightward arrow>(t) is solved by a H-infinite adaptive algorithm; and step 8, y(t) of the T sampling points is output when t>T. Signals mixed and stacked in a post nonlinear system and subject to any distribution can be separated by the NPCA-based post nonlinear blind source separation method, and the method is applicable to online blind source separation.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to an NPCA-based post-nonlinear blind source separation method. Background technique [0002] In the actual environment, many observable signals are obtained by nonlinear mixing, which is much more complicated than the linear mixing model; at the same time, due to the complexity of the mathematical theory of the nonlinear mixing system and the statistical independence of the source signals alone cannot Therefore, the linear BSS method will no longer be suitable for the case of nonlinear mixing. The mathematical models of nonlinear mixture of analog source signals mainly include: literature [1] (Table A., Jutten C. Source separation in post-nonlinear mixtures [J]. IEEE Transactions on Signal Processing, 1999, 47 (10): 2807- 2820.) in the post-nonlinear mixture model (post-nonlinear mixture, PNL), literature [2] (Woo W.L., Dlay S.S. Neural network approach to blind signal s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/03
Inventor 王荣杰詹宜巨周海峰
Owner JIMEI UNIV
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