Iterative methods for parameter estimation
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Authors
MacHardy, William R.
Subjects
Finite Impulse Response
Infinite Impulse Response
Matrix Splitting
Matrix Portioning
Toeplitz
Symmetric
Condition Number
Infinite Impulse Response
Matrix Splitting
Matrix Portioning
Toeplitz
Symmetric
Condition Number
Advisors
Tummala, Murali
Date of Issue
1990-12
Date
December 1990
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Starting with a least squares formulation of the parameter estimation problem, both fixed data and data-adaptive iterative algorithms are developed. We apply two new techniques, namely diagonal perturbation and multiple partitioning, to existing finite impulse response (FIR) and infinite impulse response (IIR) fixed data matrix splitting algorithms, resulting in improved performance. Also, we extend the fixed data algorithms to the data-adaptive case, and contrast them with FIR and IIR recursive least squares (RLS) algorithms. Computer simulations are used to evaluate the computational effectiveness of the new algorithms. We show the general rate of convergence for the algorithms, evaluate their ability to correctly represent the spectral components of simulated system frequency response in noise, and present system performance, when the order of the model is chosen to be larger than the known system order (over-modeling).
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xi, 92 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.