Tracking Targets with Bearing Data from a Single Sensor

Abstract

This report examines performance limits (Cramer-Rao bounds) on tracking a maneuvering target using bearing measurements from a single sensor on a maneuvering platform. An approximation to the Cramer-Rao bound for estimating the location, Velocity, and acceleration of a constant acceleration target with a prior distribution of the target's velocity and acceleration is derived for the case where the target and the sensor are coplanar. The bound is computed for members of a two-parameter family of sensor trajectories, and optimal sensor trajectories within this two-parameter family are identified from contour plots of the bound vs the two parameters. The optimal trajectory in most cases is a weave around the line of sight to the target, with a period which is proportional to the observation time allotted for the measurement. The bound of performance is not in general very sensitive to either the sensor's or the target's motion, or to mismatch between the two, except that the period of the sensor's weave pattern influences both the time at which good estimates become available and the variance of the estimates after a given time interval. Cramer-Rao bounds, Sensor-target geometry, Optimal angle-only tracking, Target tracking, Target acquisition, Lower bounds, Iterative least squares, Extended Kalman filter.

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Document Details

Document Type
Technical Report
Publication Date
Jan 13, 1989
Accession Number
ADA205422

Entities

People

  • W. H. Gilson

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Computations
  • Data Science
  • Estimators
  • Information Science
  • Kalman Filters
  • Line Of Sight
  • Measurement
  • Observation
  • Probability
  • Random Variables
  • Simulations
  • Standards
  • Statistical Algorithms
  • Time Intervals

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Sensor Fusion and Tracking Systems.