2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)
DOI: 10.1109/iscas.2004.1329899
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Adaptive super-exponential algorithms for blind deconvolution of MIMO systems

Abstract: Multichannel blind deconvolution of finite-impulse response (FIR) or infinite-impulse response (IIR) systems is investigated using the multichannel super-exponential method. First, some properties are shown for the rank of the correlation matrices relevant to the multichannel super-exponential method. Then, the matrix inversion lemma is extended to the degenerate rank case. Based on these results, two types of adaptive multichannel super-exponential algorithms are presented, that is, the one in covariance form… Show more

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Cited by 11 publications
(21 citation statements)
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“…For example, we may take . Because we consider the case when the number of inputs is less than the number of outputs , i.e., , the correlation matrix is not of full rank and a singular matrix [7]. Therefore, we may apply the matrix pseudoinversion lemma to the recursive equation (36).…”
Section: Application To Block-based Adaptive Blind Deconvolutionmentioning
confidence: 99%
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“…For example, we may take . Because we consider the case when the number of inputs is less than the number of outputs , i.e., , the correlation matrix is not of full rank and a singular matrix [7]. Therefore, we may apply the matrix pseudoinversion lemma to the recursive equation (36).…”
Section: Application To Block-based Adaptive Blind Deconvolutionmentioning
confidence: 99%
“…The case where the length of the block of the matrix pseudoinversion lemma is restricted to the case when the added matrix is a single dyad (i.e., is a column vector) [7]- [9]. An illustrative example and the simulation results of the matrix pseudoinversion lemma in this case and the application to adaptive blind deconvolution of MIMO systems are shown in [7]- [9].…”
Section: Remarkmentioning
confidence: 99%
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“…which is shown in [10] and its proof is found in [3]. Using this fact, the other remaining eigenvalues ofF †B are all zero.…”
Section: The Proposed Evamentioning
confidence: 59%
“…We already proposed two type of adaptive multichannel super-exponential algorithms (AMSEAs), the one in covariance (correlation or Kalman-filter) form and the other in QR-factorization form, for the degenerate rank case of the correlations matrices [8]. We propose an AMSEDA using the matrix pseudo-inversion lemma (the covariance form) in this paper, and we show the effectiveness of the proposed algorithm by computer simulations in comparison with the AMSEDA using the QR-factorization [10].…”
Section: Introductionmentioning
confidence: 99%