Survey paper: Errors-in-variables methods in system identification
Automatica (Journal of IFAC)
A quadratic programming approach to blind equalization and signal separation
IEEE Transactions on Signal Processing
Probability: Theory and Examples
Probability: Theory and Examples
Inference in Hidden Markov Models
Inference in Hidden Markov Models
Unbiased blind adaptive channel identification and equalization
IEEE Transactions on Signal Processing
Adaptive solution for blind identification/equalization usingdeterministic maximum likelihood
IEEE Transactions on Signal Processing
A least-squares approach to blind channel identification
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
On-line blind multichannel equalization based on mutually referenced filters
IEEE Transactions on Signal Processing
Fast maximum likelihood for blind identification of multiple FIRchannels
IEEE Transactions on Signal Processing
Joint order detection and blind channel estimation by least squaressmoothing
IEEE Transactions on Signal Processing
A fractionally spaced blind equalizer based on linear programming
IEEE Transactions on Signal Processing
Prediction error method for second-order blind identification
IEEE Transactions on Signal Processing
An Efficient Subspace Method for the Blind Identification of Multichannel FIR Systems
IEEE Transactions on Signal Processing
Channel Matrix Recursion for Blind Effective Channel Order Estimation
IEEE Transactions on Signal Processing
On identification of FIR systems having quantized output data
Automatica (Journal of IFAC)
Identification of ARMA models using intermittent and quantized output observations
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
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.