Preprocessing Techniques for the Estimation of Parameters from Noisy Transient Data.

Abstract

Two kinds of pole estimators for given noisy transient data are presented. One is a SVD-based methods and the other is the iterative preprocessing algorithm (IPA). The SVD method estimates the characteristic equation coefficients from the weakest eigenvectors when the system order is overdetermined. The IPA is shown to converge to the maximum-likelihood estimator for some range of SNR. It not only reduces the computational burden of the currently existing iterative algorithms but also improves stability. The approximate IPA (AIPA), which further reduces the computational burden, is described. The Cramer-Rao lower bound for the system characteristic equation coefficients is calculated. The IPA is extended to pole estimation for given multiple data sets. This technique is applied to estimating the natural frequencies of an electromagnetic scatter. An iterative scheme is described for estimating other parameters of the singularity expansion method (SEM) formalism. Keywords include: SEM; Noise reduction; Singular value decomposition; Maximum likelihood estimation technique; Multiple data set; and Transient data.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1986
Accession Number
ADA165764

Entities

People

  • Sung-won Park

Organizations

  • University of New Mexico

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Classification
  • Data Sets
  • Decomposition
  • Difference Equations
  • Estimators
  • Frequency
  • New Mexico
  • Noise Reduction
  • Resonant Frequency
  • Security
  • Simulations
  • Theses
  • United States
  • United States Government

Fields of Study

  • Engineering

Readers

  • Calculus or Mathematical Analysis
  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Neural Network Machine Learning.