Geometric Methods with Application to Robust Detection and Estimation

Abstract

We have obtained a number of results pertaining th image compression, robust estimation, and robust signal detection. All of this work has admitted the presence of data whose statistics are imperfectly known. Our results have featured adaptivity, flexibility, and nontraditional approaches. In order to employ more realistic statistical models, we have directed our research to admit nonstationarity and dependency. Much of our work in robust estimation admit nonstationarity and dependency. Much of our work in robust estimation and detection has employed a geometric approach which we have originated in past research. Our geometric techniques provide a quantitive way to measure the degree of robustness, thus offering the designer more flexibility in the meeting the performance/robustness, needs of the user. Our most recent results have resulted in the admission of essentially arbitrary dependent data, thus leading the a number of important conclusions pertaining to signal detection and the estimation of a random variable.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1989
Accession Number
ADA206999

Entities

People

  • Don R. Halverson

Organizations

  • Texas A&M University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Compression
  • Data Science
  • Detection
  • Detectors
  • Electrical Engineering
  • Estimators
  • Image Compression
  • Information Science
  • Optimal Estimators
  • Random Variables
  • Signal Detection
  • Signal Processing
  • Statistical Distributions
  • Statistics
  • Universities

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design