ESTIMATING AND DETECTING THE OUTPUTS OF LINEAR DYNAMICAL SYSTEMS

Abstract

This investigation considers three closely related problems: the optimum filtering of stationary or near-stationary random processes with unknown parameters from an infinite parameter set; estimation of the state of a linear discrete dynamical system with nongaussian noisy inputs; and applications of state estimation theory to detection. The form of the optimum filter when the parameters are unknown is found to have weights that are averages of simple functions of the signal and noise spectra averaged over the parameter space. Practical methods for implementation are given. The key problem is nonlinear state variable estimation is obtaining the joint density of the states and the observations in a convenient form.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1964
Accession Number
AD0464023

Entities

People

  • C. S. Weaver

Organizations

  • Stanford University

Tags

Communities of Interest

  • Advanced Electronics
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Adaptive Filters
  • Air Force
  • Birds
  • Computational Science
  • Data Science
  • Detection
  • Detectors
  • Equations
  • Filtration
  • Information Science
  • Linear Systems
  • Mathematical Filters
  • Military Research
  • Random Variables
  • Stationary Processes
  • Statistical Analysis
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Approximation Theory.
  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Regression Analysis.

Technology Areas

  • Space
  • Space - Space Objects