Adaptive filter theory
System identification
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
The Quadratic Eigenvalue Problem
SIAM Review
Perspectives on errors-in-variables estimation for dynamic systems
Signal Processing
A fast recursive total least squares algorithm for adaptive FIR filtering
IEEE Transactions on Signal Processing - Part I
Fast Approximate Inverse Power Iteration Algorithm for Adaptive Total Least-Squares FIR Filtering
IEEE Transactions on Signal Processing
Total least mean squares algorithm
IEEE Transactions on Signal Processing
A subspace approach to estimation of autoregressive parameters fromnoisy measurements
IEEE Transactions on Signal Processing
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This correspondence describes a method for identifying FIR models in the presence of input and output noise. The proposed algorithm takes advantage of both the bias compensation principle and the instrumental variable method. It is based on a nonlinear system of equations whose unkowns are the FIR coefficients and the input noise variance. This system allows mapping the noisy FIR identification problem into a quadratic eigenvalue problem. The identification problem is thus solved without requiring the use of iterative least-squares algorithms. The performance of the proposed approach has been tested and compared with that of other identification methods by means of Monte Carlo simulations.