Blind system identification using precise and quantized observations

This paper studies the blind identification of multi-channel FIR systems using precise and quantized observations. First, a new deterministic blind identification (DBI) algorithm is presented for multi-channel FIR systems using precise observations, in which the system parameters can be consistently estimated and the common source signal can be stably recovered. When the observed samples are quantized by a static finite-level quantizer, an iterative deterministic blind identification (IDBI) method is then provided. The asymptotic characters of the proposed IDBI method are discussed and the quantization effect on the identification performance is analyzed. Numerical simulations are given to support the developed DBI method and IDBI method.

[1]  Zhi Ding,et al.  A Quadratic Programming Approach to Blind Equalization and Signal Separation , 2009, IEEE Transactions on Signal Processing.

[2]  T. Kailath,et al.  A least-squares approach to blind channel identification , 1995, IEEE Trans. Signal Process..

[3]  E. Bai,et al.  Block Oriented Nonlinear System Identification , 2010 .

[4]  David Gesbert,et al.  On-line blind multichannel equalization based on mutually referenced filters , 1997, IEEE Trans. Signal Process..

[5]  Eric Moulines,et al.  Subspace methods for the blind identification of multichannel FIR filters , 1995, IEEE Trans. Signal Process..

[6]  Cishen Zhang,et al.  Blind Identification of Multi-Channel ARMA Models Based on Second-Order Statistics , 2012, IEEE Transactions on Signal Processing.

[7]  G. Yin,et al.  System Identification with Quantized Observations , 2010 .

[8]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[9]  Yoram Bresler,et al.  Exact maximum likelihood parameter estimation of superimposed exponential signals in noise , 1986, IEEE Trans. Acoust. Speech Signal Process..

[10]  Konstantinos I. Diamantaras,et al.  An Efficient Subspace Method for the Blind Identification of Multichannel FIR Systems , 2008, IEEE Transactions on Signal Processing.

[11]  Yingbo Hua,et al.  Previously Published Works Uc Riverside Title: Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels Fast Maximum Likelihood for Blind Identification of Multiple Fir Channels , 2022 .

[12]  Cishen Zhang,et al.  A blind deconvolution approach to ultrasound imaging , 2012, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[13]  David Gesbert,et al.  Unbiased blind adaptive channel identification and equalization , 2000, IEEE Trans. Signal Process..

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  Philippe Loubaton,et al.  Prediction error method for second-order blind identification , 1997, IEEE Trans. Signal Process..

[16]  Mila Nikolova,et al.  Adaptive solution for blind identification/equalization using deterministic maximum likelihood , 2002, IEEE Trans. Signal Process..

[17]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[18]  L. Tong,et al.  Multichannel blind identification: from subspace to maximum likelihood methods , 1998, Proc. IEEE.

[19]  Graham C. Goodwin,et al.  On identification of FIR systems having quantized output data , 2011, Autom..

[20]  Minyue Fu,et al.  Identification of ARMA models using intermittent and quantized output observations , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Jian-Kang Zhang,et al.  A fractionally spaced blind equalizer based on linear programming , 2002, IEEE Trans. Signal Process..

[22]  R. Durrett Probability: Theory and Examples , 1993 .

[23]  T. Engin Tuncer,et al.  Channel Matrix Recursion for Blind Effective Channel Order Estimation , 2011, IEEE Transactions on Signal Processing.

[24]  Lang Tong,et al.  Joint order detection and blind channel estimation by least squares smoothing , 1999, IEEE Trans. Signal Process..

[25]  Eric Moulines,et al.  Inference in hidden Markov models , 2010, Springer series in statistics.

[26]  E. Lehmann Elements of large-sample theory , 1998 .

[27]  Ning Han,et al.  A Blind OFDM Detection and Identification Method Based on Cyclostationarity for Cognitive Radio Application , 2009, IEICE Trans. Commun..

[28]  Joel Max,et al.  Quantizing for minimum distortion , 1960, IRE Trans. Inf. Theory.

[29]  Torsten Söderström,et al.  Errors-in-variables methods in system identification , 2018, Autom..

[30]  Torsten Söderström,et al.  Accuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables and output error identification , 2010, Autom..