A Study of Line of Bearing Errors

Abstract

Many systems use Line-of-Bearing (LOB) information to locate distant objects. The errors in location due to noisy LOB measurements are the focus of this report. A central assumption for these systems is that the LOB location errors are adequately modeled by bivariate Gaussian distributions. These systems typically use weighted averages to estimate a target's location. A discussion in Thompson and Durfee (1992) illustrates that these location errors are not necessarily Gaussian. In some situations, reasonable errors lead to calculation of implausible target locations. In addition, it was demonstrated that the location distribution is skewed in the direction of increasing range; thus, bias is a problem. This report investigates the properties of the errors associated with LOB locations. Guidelines are suggested for various assumptions about LOB location errors. The concept of the stability ratio is introduced and used to determine the possibility of encountering implausible LOB locations; as a means of predicting the existence and magnitude of estimator bias; to determine the applicability of using a bivariate normal distribution for modeling LOB errors; and for determining the utility of closed-form models to predict the covariance of LOB locations. Line of bearing, Target detection, Angle of arrival.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA270703

Entities

People

  • Andrew A. Thompson Iii
  • Gary L. Durfee

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Angle Of Arrival
  • Covariance
  • Data Science
  • Detection
  • Estimators
  • Gaussian Distributions
  • Geometry
  • Information Science
  • Measurement
  • Military Research
  • Normal Distribution
  • Probability
  • Security
  • Simulations
  • Standards
  • Target Detection
  • Two Dimensional

Readers

  • Approximation Theory.
  • Sensor Fusion and Tracking Systems.
  • Statistical inference.