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Performance study of robust cross relation method for blind channel identification

Qi Cheng
TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region  
The robust CR method is an improved version of the CR method, and exploits a greatest common divisor (GCD) algorithm. Its performance is investigated in this paper.  ...  The robust CR method is shown to have a similar estimation accuracy as the subspace method by Mouline et al but a lower computational complexity.  ...  Qiu for providing FORTRAN code, and Dr. Y. Hua for helpful discussion. This work was supported by the UWS Nepean research grant, UWS study leave program and DRG program.  ... 
doi:10.1109/tencon.2003.1273433 fatcat:xvc67luw4zcgbfpn7g2pdliibq

Blind system identification using cross-relation methods: further results and developments

A. Aissa-El-Bey, M. Grebici, K. Abed-Meraim, A. Belouchrani
2003 Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.  
Blind system identification using cross-relation methods : further results and developments.  ...  The CR method introduced in [1] is one of the simplest and efficient methods for blind identification of FIR SIMO systems.  ...  It was shown that based on ¢ ¤ ¢ % Ë # possible cross-relations, the channel parameters can be uniquely identified according to [5] : Theorem ABSTRACT We consider the problem of blind identification  ... 
doi:10.1109/isspa.2003.1224787 dblp:conf/isspa/Aissa-El-BeyGAB03 fatcat:yklxxihs3zhhvcnoki5el3fefy

Distributed blind system identification in sensor networks

Chengpu Yu, Lihua Xie, Yeng Chai Soh
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This paper studies the blind identification of multi-channel FIR systems in the context of sensor networks.  ...  Index Terms-Blind identification, multi-agent system, consensus based gradient method.  ...  The cross relation equation does not contain the source signal and is linear with respect to h i ; thus, it is often used for the blind identification of practical systems.  ... 
doi:10.1109/icassp.2014.6854567 dblp:conf/icassp/YuXS14 fatcat:7iy3u55fdnfmfg57qtoaf42vku

Weighted Semi-Blind Channel Identification by Cross Relation Method

Mohamed Tahar Taba, S. Femmam
2009 American Journal of Applied Sciences  
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.  ...  Linear approximation of the GMSK signal made the blind equalization system model applicable for GSM.  ...  In this study, we first present a new blind channel identification algorithm based on the cross relation method [1] .  ... 
doi:10.3844/ajassp.2009.1264.1269 fatcat:kb5a2lmsp5h4hesfejkopvvyke

A least squares component normalization approach to blind channel identification

C. Avendano, J. Benesty, D.R. Morgan
1999 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)  
We describe a new method for blind system identification that uses the cross relation properties between two or more sensor signals to estimate the impulse responses of the channels.  ...  The method performs as well or better than other similar blind identification techniques under noisy and ill-conditioned channel conditions, and is computationally simpler to implement.  ...  for fast blind identification.  ... 
doi:10.1109/icassp.1999.758269 dblp:conf/icassp/AvendanoBM99 fatcat:xymgjft3nvhitphl4652kjl27m

Using the Pearson correlation coefficient to develop an optimally weighted cross relation based blind SIMO identification algorithm

Yiteng Huang, Jacob Benesty, Jingdong Chen
2009 2009 IEEE International Conference on Acoustics, Speech and Signal Processing  
Blind SIMO identification is challenging when additive noise is strong and for ill-conditioned/acoustic SIMO systems.  ...  A weighted cross relation (CR) algorithm presumably can be robust to noise but there lacks a practical way to define the weights.  ...  The focus of the current blind SIMO identification research is primarily on the SOS-based methods.  ... 
doi:10.1109/icassp.2009.4960293 dblp:conf/icassp/HuangBC09 fatcat:gtkvq3qusvau5esetxuwvschpu

A class of frequency-domain adaptive approaches to blind multichannel identification

Yiteng Huang, J. Benesty
2003 IEEE Transactions on Signal Processing  
In this paper, we extend our previous studies on adaptive blind channel identification from the time domain into the frequency domain.  ...  Simulations show that the frequency-domain adaptive approaches perform as well as or better than their time-domain counterparts and the cross-relation (CR) batch method in most practical cases.  ...  Morgan for carefully reading a draft and providing many constructive comments and suggestions that have improved the clarity of this paper.  ... 
doi:10.1109/tsp.2002.806559 fatcat:juytxhk7xrharljaakgvpe4bg4

Blind Modulation Identification of Underwater Acoustic MPSK Using Sparse Bayesian Learning and Expectation Maximization

Tao Fang, Zhi Xia, Songzuo Liu, Xiongbiao Wu, Lanyue Zhang
2020 Applied Sciences  
new idea for modulation identification of non-cooperative underwater acoustic MPSK.  ...  The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the  ...  In [25] , a least square blind channel estimation method is proposed and the necessary condition for channel estimation based on cross-relations is analyzed in SIMO channel.  ... 
doi:10.3390/app10175919 fatcat:pry2ftz45jaqjaisaoqgx2w2va

Adaptive blind estimation of sparse SIMO channels

Abdeldjalil Aissa-El-Bey, Karim Abed-Meraim, Christophe Laot
2011 International Workshop on Systems, Signal Processing and their Applications, WOSSPA  
In this paper, we focus on the adaptive identification of sparse SIMO channels in a blind context.  ...  The SCR method proceeds as follows : at first a blind approach based on the crossrelation criterion is derived for channel estimation.  ...  Sparse Cross-Relations method In this section, we propose an iterative algorithm for the identification of sparse channels in the SIMO system case, namely the Sparse Cross-Relations method (SCR).  ... 
doi:10.1109/wosspa.2011.5931508 fatcat:xvdv32wtdjd4dhfnwgjmtx55fe

Multichannel blind identification: from subspace to maximum likelihood methods

Lang Tong, S. Perreau
1998 Proceedings of the IEEE  
A review of recent blind channel estimation algorithms is presented.  ...  This review serves as an introductory reference for this currently active research area.  ...  The persistent excitation of the source, along with the coprime condition of subchannels certainly also ensures the identifiability. 2) The Cross Relation Approach: The cross relation (CR) approach, a  ... 
doi:10.1109/5.720247 fatcat:uikzvmqrgrf2dh6dwosmr34z7i

A Frequency Domain Method for Blind Identification of Timing Mismatches in Time-Interleaved ADCs

Christian Vogel
2006 2006 NORCHIP  
We introduce an accurate blind timing mismatch identification for bandlimited, oversampled input signals, which is based on a frequency relation between the input signal and the sampled output signal and  ...  The performance of time-interleaved ADCs (TI-ADCs) mainly suffers from timing mismatches.  ...  CONCLUSION We have presented a blind timing mismatch identification method for time-interleaved ADCs.  ... 
doi:10.1109/norchp.2006.329241 fatcat:mq4xwp6elfbjrb6ccbeuqqi7iy

Blind Sparse-Nonnegative (BSN) Channel Identification for Acoustic Time-Difference-of-Arrival Estimation

Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel D. Lee
2007 2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics  
Blind channel identification approaches for TDOA estimation explicitly model multipath reflections and have been demonstrated to be effective in dealing with reverberation.  ...  Unfortunately, existing blind channel identification algorithms are sensitive to ambient noise.  ...  The blind channel identification via cross relation is based on a clever observation, x2(k) * h1 = x1(k) * h2 = s(k) * h1 * h2, if the microphone signals are noiseless [8] .  ... 
doi:10.1109/aspaa.2007.4392996 fatcat:ncagq2hq6vestgtwo2mxbgwkla

An Efficient Identification Algorithm in a Low SNR Channel
저 SNR을 갖는 채널에서 효율적인 인식 알고리즘

Jeewon Hwang, Juphil Cho
2014 The Journal of the Korean Institute of Information and Communication Engineering  
Identification of communication channels is a problem of important current theoretical and practical concerns.  ...  Proposed technique shows the better performance than one of existing algorithms .  ...  Blind channel identification technique has been developed in adaptive algorithm based on vector-correlation method [8, 9, 11] . But most algorithms neglected the effect of channel noise.  ... 
doi:10.6109/jkiice.2014.18.4.790 fatcat:cj7yemdtrfbkjjjnnn422xmgyy

Deterministic approaches for blind equalization of time-varying channels with antenna arrays

Hui Liu, G.B. Giannakis
1998 IEEE Transactions on Signal Processing  
The multiple inputs are related through the bases, thereby allowing blind equalization to be accomplished without the use of higher order statistics.  ...  Two deterministic blind equalization approaches are presented: One determines the channels first and then the equalizers, whereas the other estimates the equalizers directly.  ...  The second method, on the other hand, estimates the equalizers in one step by taking advantage of the cross-relations of the antenna outputs [18] .  ... 
doi:10.1109/78.726813 fatcat:6jmrc4lbcfbtjn3t2kacsoejhu

A Cross-Relation Based Affine Projection Algorithm For Blind Simo System Identification

J. Benesty, Emanuel Habets, P.A Naylor
2011 Zenodo  
Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011  ...  The cross-relation (CR) method [2] , was one of the first methods proposed to blindly identify a SIMO system and has served as the foundation for several algorithms.  ...  Other SOS methods for blind SIMO system identification are for example subspace methods [3, 4] and the prediction error method [5] .  ... 
doi:10.5281/zenodo.42678 fatcat:fxnr7huzz5fvlkfo4cyq6mifcy
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