Probability and Statistics in Sensor Performance Modeling

Abstract

Signals from many military targets of interest are often strongly randomized, due to the irregular mechanisms by which the signals are generated and propagated. In particular, complicated and dynamic terrestrial/atmospheric environments (with man-made objects, vegetation, and turbulence) randomize signals through random atmospheric and terrestrial processes affecting the propagation. Signals may also be considered random due to uncertainties in the knowledge of the propagation environment and target-sensor geometry. Predictions of sensor performance and recommendations of sensor types and placements derived from them, thus, should account for the random nature of the sensed signals. This report discusses software-modeling approaches for characterizing signals subject to random generation and propagation mechanisms. By representing signals with random variables, they are manipulated statistically to make probabilistic predictions of sensor performance. Both the theory and implementation in a general, object-oriented software design for battlefield signal transmission and sensing is explained. The Java-language software program is called Environmental Awareness for Sensor and Emitter Employment. Some important numerical issues in the implementation are also discussed.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2010
Accession Number
ADA533718

Entities

People

  • Chris L. Pettit
  • D. K. Wilson
  • Kenneth K. Yamamoto

Organizations

  • Cold Regions Research and Engineering Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Computer Programming
  • Data Science
  • Detection
  • Distribution Functions
  • Employment
  • Gaussian Distributions
  • Information Science
  • Language
  • Probability
  • Probability Distributions
  • Random Variables
  • Situational Awareness
  • Software Design
  • Statistical Analysis
  • Statistics
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.
  • Wave Propagation and Nonlinear Chaotic Dynamics.