A New Approach to Multitarget Tracking Using Probabilistic Data Association

Abstract

This report develops the theory for a multitarget tracking algorithm based on Probabilistic Data Association with new selection rules for assigning sensor measurements to target track and for forming multitrack clusters. These new rules remove the requirement to form a gate about each target's predicted position for the selection of sensor measurements. The resultant algorithm is the same for all target tracks and clutter conditions. The algorithm adapts to the sensor measurements via probability terms which model the environment and sensor processing. Keywords: Automatic tracking; Kalman filtering; Probability theory; Probabilistic data association; Estimation; Australia.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1986
Accession Number
ADA179437

Entities

People

  • C. B. Colegrove

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Australia
  • Automatic Tracking
  • Data Association
  • Department Of Defense
  • Electronics
  • Environment
  • Kalman Filtering
  • Measurement
  • Multitarget Tracking
  • Probability
  • Statistical Algorithms

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design