Statistical Inference for Change-of-Aperture Problems in Command and Control

Abstract

A map is a powerful way of summarizing spatial data; in the arena of Command and Control (C2), great maps can produce knowledgable command decisions. Two approaches to statistically optimal mapping are taken. The first develops spatial multiresolution Kalman filtering of data at various apertures, and the second develops constrained optimal spatial prediction to answer nonlinear C2 questions consistently. Finally, the temporal component is introduced to allow updating of current maps based on newly acquired C2 data.

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Document Details

Document Type
Technical Report
Publication Date
Sep 09, 2002
Accession Number
ADA405690

Entities

People

  • Noel Cressie

Organizations

  • Ohio State University

Tags

Communities of Interest

  • C4I
  • Space

DTIC Thesaurus Topics

  • Bayesian Networks
  • Command And Control
  • Computational Science
  • Computer Programs
  • Data Science
  • Estimators
  • Infectious Diseases
  • Information Processing
  • Information Science
  • Markov Models
  • Models
  • Monte Carlo Method
  • Probability
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Joint Military Operations and Doctrine.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Fully Networked C3
  • Fully Networked C3 - Command and Control