Blind system identification

Abed-Meraim, Karim; Qiu, Wanzhi; Hua, Yingbo

doi:10.1109/5.622507

Cited by 295 publications

(222 citation statements)

References 98 publications

(108 reference statements)

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“…However, the strategy outlined above can be extended to blind system identification (BSI), also known as equalization, in which the outputs are linear convolutive mixtures of the inputs [30]. More specifically, the same strategy can be applied to the identification of finite impulse response (FIR) systems of the form:…”

Section: Future Researchmentioning

confidence: 99%

A novel deterministic method for large-scale blind source separation

Bousse

Debals

Lathauwer

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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A novel deterministic method for blind source separation is presented. In contrast to common methods such as independent component analysis, only mild assumptions are imposed on the sources. On the contrary, the method exploits a hypothesized (approximate) intrinsic low-rank structure of the mixing vectors. This is a very natural assumption for problems with many sensors. As such, the blind source separation problem can be reformulated as the computation of a tensor decomposition by applying a low-rank approximation to the tensorized mixing vectors. This allows the introduction of blind source separation in certain big data applications, where other methods fall short.

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Section: Future Researchmentioning

confidence: 99%

A novel deterministic method for large-scale blind source separation

Bousse

Debals

Lathauwer

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…The aim of BSI is to estimate blindly the impulse responses hm from the observations xm(n). Among various BSI algorithms, SOS-based algorithms have become popular [8]. A typical approach is to utilize the crossrelation (CR) between two channels [3], i.e., x…”

Section: Problem Formulationmentioning

confidence: 99%

Blind system identification for speech dereverberation with Forced Spectral Diversity

Lin

Khong

Naylor

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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The common zeros problem for blind system identification (BSI) is well known. It degrades the performance of classic BSI algorithms and therefore imposes the limit on the performance of subsequent speech dereverberation. The effect of near-common zeros has recently been studied in terms of channel diversity and the degradation in performance of BSI and multichannel equalization algorithms has been shown. We now introduce a novel approach to improve channel diversity which we refer to as Forced Spectral Diversity (FSD). The FSD concept uses a combination of spectral shaping filters and effective channel undermodelling. Simulation results show that the proposed approach achieves improved performance with reduced complexity for multichannel BSI in a room acoustics example.

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“…In this study, we first present a new blind channel identification algorithm based on the cross relation method [1] . We note that blind channel identification algorithms based on SOS are sensitive to channel order mismatch [2] , which is very common in wireless environment.…”

Section: Introductionmentioning

confidence: 99%

Weighted Semi-Blind Channel Identification by Cross Relation Method

Taba¹,

Femmam²

2009

American J. of Applied Sciences

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Problem statement:The bandwidth efficiency of many communication systems could be improved if the transmission channel was estimated blindly without resort to training sequences. GMSK was a spectrum-efficient modulation scheme and it was adopted as the modulation standard of GSM systems. Approach: However, because of its phase modulation, Gaussian filtering and partial response signalling properties, Results: The simulation results showed great potential of semi-blind identification algorithms, since we used no extra antenna or over-sampling the received signal. Conclusion: GMSK was not a linear modulation. Linear approximation of the GMSK signal made the blind equalization system model applicable for GSM. In the sequel, a linear approximation of GMSK signals was presented and a blind GSM and semi-blind channel identification algorithm based on the cross relation method was suggested.

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Blind system identification

Cited by 295 publications

References 98 publications

A novel deterministic method for large-scale blind source separation

A novel deterministic method for large-scale blind source separation

Blind system identification for speech dereverberation with Forced Spectral Diversity

Weighted Semi-Blind Channel Identification by Cross Relation Method

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