Detection of Randomness in Sparse Data Set of Three Dimensional Time Series Distributions

Abstract

A two-stage method is provided for automatically characterizing the spatial arrangement among data points of a three-dimensional time series distribution in a data processing system wherein the classification of said time series distribution is required. The two-stage method utilizes Cartesian grids to determine the following: (1) the number of cubes in the grids containing at least one input data point of the time series distribution, (2) the expected number of cubes that would contain at least one data point in a statistically determined random distribution in said grids, and (3) an upper and lower probability of false alarm above and below said expected value utilizing a second discrete probability relationship to analyze the randomness characteristic of the input time series distribution. (5 figures)

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Document Details

Document Type
Technical Report
Publication Date
Oct 06, 2003
Accession Number
ADD020117

Entities

People

  • Francis J. O'brian Jr

Organizations

  • United States Department of the Navy

Tags

Communities of Interest

  • Air Platforms
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Binomials
  • Cartesian Coordinates
  • Classification
  • Data Processing
  • Data Sets
  • Detection
  • False Alarms
  • Information Processing
  • Mathematical Analysis
  • Operating Systems
  • Probability
  • Probability Distributions
  • Three Dimensional
  • Undersea Warfare
  • Warfare
  • Warning Systems
  • White Noise

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Regression Analysis.