System identification
New algorithm for optimal parameter estimation with linear constraints
Journal of Optimization Theory and Applications
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Estimation of Constrained Parameters With Guaranteed MSE Improvement
IEEE Transactions on Signal Processing
Minimum variance estimation of parameters constrained by bounds
IEEE Transactions on Signal Processing
On maximum-likelihood estimation of difference equation parameters
IEEE Transactions on Signal Processing
Maximum likelihood trend estimation in exponential noise
IEEE Transactions on Signal Processing
Improved estimation performance using known linear constraints
Automatica (Journal of IFAC)
Estimation of constrained parameters in a linear model with multiplicative and additive noise
IEEE Transactions on Information Theory
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This paper deals with the constrained system identification problem of linear discrete time dynamical systems. It is assumed that the parameters of the system are constrained due to physical limitations. Using a multiple projection approach, a minimum variance estimator and its associated recursive version are developed to estimate the constrained parameters of the system. The most important feature of the developed technique is that it gives estimates of the system parameters once the first set of measurements is received. Therefore, there is no need to wait till sufficient number of measurements are collected to start the algorithm. The simulation results of illustrative examples are presented to show the effectiveness of the proposed estimation scheme.