An Adaptive Distributed-Measurement Extended Kalman Filter for a Short Range Tracker. Volume I.

Abstract

An adaptive Extended Kalman Filter algorithm is designed to track a distributed (elliptical) source target in a closed loop tracking problem, using outputs from a forward looking infrared (FLIR) sensor as measurements. The filter adaptively estimates image intensity, target size and shape, dynamic driving noise, and translational position changes due to two effects: actual target motion, and atmospheric jitter. Atmospheric backgrounds are studied for the effect of temporal and spatial correlations on filter performance. A Monte Carlo analysis is conducted to determine filter performance for two target scenarios: approximately straight approach and cross range constant velocity. Good performance is obtained for the first two trajectories. For the second trajectory, a one sigma tracking error of .2 pixel (4 microrad) with a signal to noise ratio of 12.5. The filter adapts well to changes in image intensity, size, and shape. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1979
Accession Number
ADA080249

Entities

People

  • Douglas Alan Harnly
  • Robert L. Jensen

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Human Systems
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computational Science
  • Computer Simulations
  • Data Analysis
  • Detection
  • Detectors
  • Electromagnetic Radiation
  • Estimators
  • Filters
  • Infrared Detectors
  • Kalman Filters
  • Lasers
  • Mathematical Filters
  • Statistical Algorithms
  • Three Dimensional
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Pulsed Power and Plasma Physics.
  • Sensor Fusion and Tracking Systems.