A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS OF COMPLEX VALUED SIGNALS

2000 ◽  
Vol 10 (01) ◽  
pp. 1-8 ◽  
Author(s):  
ELLA BINGHAM ◽  
AAPO HYVÄRINEN
Keyword(s):  
Fixed Point ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fixed Point Algorithm ◽  
Local Consistency ◽  
Type Algorithm ◽  
Source Signals ◽  
Complex Valued ◽  
Point Type

Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast fixed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational efficiency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.

scholarly journals Adaptive Complex-Valued Independent Component Analysis Based on Second-Order Statistics

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yanfei Jia ◽  
Xiaodong Yang
Keyword(s):  
Independent Component Analysis ◽  
Cost Function ◽  
Order Statistics ◽  
Component Analysis ◽  
Second Order ◽  
Independent Component ◽  
Second Order Statistics ◽  
The Cost ◽  
Source Signals ◽  
Complex Valued

This paper proposes a two-stage fast convergence adaptive complex-valued independent component analysis based on second-order statistics of complex-valued source signals. The first stage constructs a cost function by extending the real-valued whiten cost function to a complex-valued domain and optimizes the cost function using a complex-valued gradient. The second stage uses the restriction that the pseudocovariance matrix of the separated signal is a diagonal matrix to construct the cost function and the geodesic method is used to optimize the cost function. Compared with other adaptive complex-valued independent component analysis, the proposed method shows a faster convergence rate and smaller error. Computer simulations were performed on synthesized signals and communications signals. The simulation results demonstrate the validity of the proposed algorithm.

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