Orthogonal Lattice Modeling of Nonlinear Systems.

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. Keywords: Autoregressive; Deconvolution; Lattice Filter; Nonlinear Lattice Filter; Kalman Filter; Least-squares Inverse Filter; Nonlinear Deconvolution. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1986
Accession Number
ADA175147

Entities

People

  • Scot L. Johnson

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computers
  • Electrical Engineering
  • Engineering
  • Estimators
  • Filters
  • Filtration
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Nonlinear Systems
  • Optimal Estimators
  • Recursive Filters
  • Signal Processing
  • Simulations
  • Statistical Algorithms

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.