A statistical approach to subspace based blind identification

Blind identification of single input multiple output systems is considered herein. The low-rank structure of the output signal is exploited to blindly identify the channel using a subspace fitting framework. Two approaches based on a minimal linear parameterization of a subspace are presented and analyzed. The asymptotically best consistent estimate is derived for the class of blind subspace-based techniques. The asymptotic estimation error covariance of the subspace estimates is derived, and the corresponding covariance of the statistically optimal estimates provides a lower bound on the estimation error covariance of subspace methods. A two-step procedure involving only linear systems of equations is presented that asymptotically achieves the bound. Simulations and numerical examples are provided to compare the two approaches.

[1]  Philippe Loubaton,et al.  On subspace methods for blind identification of single-input multiple-output FIR systems , 1997, IEEE Trans. Signal Process..

[2]  Alexander Graham,et al.  Kronecker Products and Matrix Calculus: With Applications , 1981 .

[3]  D. Slock,et al.  Blind subspace identification of a BPSK communication channel , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

[4]  Sandro Bellini,et al.  Blind equalization and deconvolution , 1991, Optics & Photonics.

[5]  D.T.M. Slock.  Subspace techniques in blind mobile radio channel identification and equalization using fractional spacing and/or multiple antennas , 1995 .

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

[7]  Lang Tong,et al.  Fast blind equalization via antenna arrays , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Hui Liu,et al.  Recent developments in blind channel equalization: From cyclostationarity to subspaces , 1996, Signal Process..

[9]  C.R. Johnson,et al.  Admissibility in blind adaptive channel equalization , 1991, IEEE Control Systems.

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

[11]  Constantinos B. Papadias,et al.  Blind fractionally-spaced equalization based on cyclostationarity , 1994, Proceedings of IEEE Vehicular Technology Conference (VTC).

[12]  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 .

[13]  Petre Stoica,et al.  Markov-based eigenanalysis method for frequency estimation , 1994, IEEE Trans. Signal Process..

[14]  Yingbo Hua,et al.  Performance analysis of the subspace method for blind channel identification , 1996, Signal Process..

[15]  Torsten Söderström,et al.  Statistical analysis of MUSIC and subspace rotation estimates of sinusoidal frequencies , 1991, IEEE Trans. Signal Process..

[16]  Lang Tong,et al.  A new approach to blind identification and equalization of multipath channels , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[17]  Björn E. Ottersten,et al.  Statistical analysis of a subspace method for blind channel identification , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[18]  P. Stoica,et al.  On the expectation of the product of four matrix-valued Gaussian random variables , 1988 .

[19]  Jitendra K. Tugnait,et al.  On blind identifiability of multipath channels using fractional sampling and second-order cyclostationary statistics , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

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

[21]  M. Kristensson,et al.  Asymptotic comparison of two blind channel identification algorithms , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[22]  K. S. Banerjee.  Generalized Inverse of Matrices and Its Applications , 1973 .

[23]  Philippe Loubaton,et al.  Subspace methods for blind identification of SIMO-FIR systems , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[24]  A. Benveniste,et al.  Blind Equalizers , 1984, IEEE Trans. Commun..