Final Project Report for University of Notre Dame, Department of Electrical and Computer Engineering, Contract N00014-87-K-0284

Abstract

Recent advances in integrated circuits and signal processing technology have prompted the need of algorithms with higher degrees of modularity and pipeline-ability, resulting in higher degrees of concurrency and machine perception. It is generally believed in the research community that higher degrees of concurrency and machine perception will bring forth many breakthroughs in many signal processing areas, particularly in adaptive systems, speech and image processing and recognition. The objective of this research project is to develop an adaptive signal processing architecture with important features such as modularity, pipeline-ability, and intelligent use of information. The ground work upon which this research project rests is a recursive parameter estimation algorithm, i.e., the so-called OBE algorithm, which features a discerning update strategy. This discerning update is in sharp contrast to the continual update used by most existing algorithms. The estimation algorithm has been developed with a set-theoretic framework. In particular, starting with the assumption that the underlying noise (of the system being studied) is bounded in magnitude, a recursive least-squares type of estimation algorithm was obtained with a discerning update strategy. An important outcome of such discerning updates is that the resulting algorithm can be implemented with two modules: An information processor followed by an updating processor.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 1989
Accession Number
ADA205787

Entities

People

  • Ruey-wen Liu
  • Yih-fang Huang

Organizations

  • University of Notre Dame

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Adaptive Filters
  • Adaptive Systems
  • Artificial Intelligence
  • Blood Coagulation Factors
  • Computational Complexity
  • Control Systems
  • Data Science
  • Difference Equations
  • Electrical Engineering
  • Engineering
  • Estimators
  • Information Science
  • Machine Perception
  • Pattern Recognition
  • Reliability
  • Signal Processing
  • Statistical Analysis

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Systems Analysis and Design