Modified Projection-Type Methods for Monotone Variational Inequalities
SIAM Journal on Control and Optimization
A New Algorithm for Solving Strictly Convex Quadratic Programs
SIAM Journal on Optimization
Fitting ARMA Models to linear non-Gaussian processes using higher order statistics
Signal Processing - Image and Video Coding beyond Standards
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
A constrained sequential EM algorithm for speech enhancement
Neural Networks
Convergence analysis of the sign algorithm without the independenceand gaussian assumptions
IEEE Transactions on Signal Processing
A peak preserving algorithm for the removal of colored noise from signals
IEEE Transactions on Signal Processing
On a least-squares-based algorithm for identification of stochasticlinear systems
IEEE Transactions on Signal Processing
Parameter estimation for autoregressive Gaussian-mixture processes: the EMAX algorithm
IEEE Transactions on Signal Processing
System parameter estimation with input/output noisy data andmissing measurements
IEEE Transactions on Signal Processing
Fast adaptive algorithms for AR parameters estimation using higherorder statistics
IEEE Transactions on Signal Processing
Support vector method for robust ARMA system identification
IEEE Transactions on Signal Processing
Comparison of some instrumental variable methods-Consistency and accuracy aspects
Automatica (Journal of IFAC)
A new look at entropy for solving linear inverse problems
IEEE Transactions on Information Theory
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In this paper, a novel noise-constrained least-squares (NCLS) method for online autoregressive (AR) parameter estimation is developed under blind Gaussian noise environments, and a discrete-time learning algorithm with a fixed step length is proposed. It is shown that the proposed learning algorithm converges globally to an AR optimal estimate. Compared with conventional second-order and high-order statistical algorithms, the proposed learning algorithm can obtain a robust estimate which has a smaller mean-square error than the conventional least-squares estimate. Compared with the learning algorithm based on the generalized least absolute deviation method, instead of minimizing a non-smooth linear L"1 function, the proposed learning algorithm minimizes a quadratic convex function and thus is suitable for online parameter estimation. Simulation results confirm that the proposed learning algorithm can obtain more accurate estimates with a fast convergence speed.