Orthogonal lattice modeling of nonlinear systems
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Authors
Johnson, Scot Lee
Subjects
Autoregressive
Deconvolution
Lattice filter
Nonlinear lattice filter
Kalman filter
Least-squares inverse filter
Nonlinear deconvolution
Deconvolution
Lattice filter
Nonlinear lattice filter
Kalman filter
Least-squares inverse filter
Nonlinear deconvolution
Advisors
Parker, Sydney R.
Date of Issue
1986-09
Date
September 1986
Publisher
Language
en_US
Abstract
The application of analysis lattice filters to the problem of determining the input to a system from observations of the system's output (i.e., deconvolution) is discussed. Both linear and nonlinear systems are considered. Lattice filter modeling algorithms (Levinson and Schur) are presented.
The theory of least-squares inverse filters is reviewed. This leads to a discussion of the lattice filter, which in turn leads to the Generalized Lattice Theory. The Generalized Lattice Theory is then used to develop a nonlinear lattice structure. Simulations show that the nonlinear lattice is an effective inverse filter for both linear and nonlinear systems.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
142 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.