A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--

Nari TANABE  Toshihiro FURUKAWA  Kohichi SAKANIWA  Shigeo TSUJII  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E88-A   No.7   pp.2015-2018
Publication Date: 2005/07/01
Online ISSN: 
DOI: 10.1093/ietfec/e88-a.7.2015
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Digital Signal Processing
Keyword: 
principal component analysis,  subspace method,  colored observation noise,  noise variance,  channel order,  computational complexity,  

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Summary: 
We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.