Multiple Sampling for Estimation on a Finite Horizon

Abstract

We discuss some multiple sampling problems that arise in real-time estimation problems with limits on the number of samples. The quality of estimation is measured by an aggregate squared error over a finite horizon. We compare the performances of the best deterministic, level-triggered and optimal sampling schemes. We restrict the signal to be either a Wiener process or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions. For the Ornstein-Uhlenbeck process, we provide procedures for numerical computation. Our results show that level-triggered sampling is almost optimal when the signal is stable.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA446968

Entities

People

  • George V. Moustakides
  • John Baras
  • Maben Rabi

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Boundary Value Problems
  • Brownian Motion
  • Computations
  • Contour Integrals
  • Control Systems
  • Detectors
  • Distortion
  • Engineering
  • Filtration
  • Intervals
  • Numbers
  • Observation
  • Sampling
  • Sequences
  • Signal Detection
  • Statistical Sampling
  • Universities

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Systems Analysis and Design