Estimating Random Integrals from Noisy Observations: Sampling Designs and Their Performance.

Abstract

The problem of estimating a weighted average of a random process from noisy observations at a finite number of sampling points is considered. The performance of sampling designs with optimal or suboptimal, but easily computable, estimator coefficients is studied. Several examples and special cases are studied included additive independent noise, nonlinear distortion with noise, and quantization noise. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1985
Accession Number
ADA170330

Entities

People

  • James A. Bucklew
  • Stamatis Cambanis

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Covariance
  • Delta Functions
  • Engineering
  • Equations
  • Estimators
  • Information Theory
  • Integral Equations
  • Markov Processes
  • North Carolina
  • Notation
  • Optimal Estimators
  • Random Variables
  • Signal Processing
  • Stationary Processes
  • Statistics
  • Stochastic Processes

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.