Probabilistic Multi-Hypothesis Tracking

Abstract

In a multitarget, multimeasurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. This study is a probabilistic approach to the measurement-to-track assignment problem. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; Instead, the probability that each measurement belongs to each track is estimated using a maximum a posteriori (MAP) method. These measurement-to-track probability estimates are intrinsic to the multitarget tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning. The PMHT algorithm is an optimal MAP multitarget tracking algorithm. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Feb 15, 1995
Accession Number
ADA298501

Entities

People

  • Roy L. Streit
  • Tod Luginbuhl

Organizations

  • Naval Underwater Systems Center

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Bayes Theorem
  • Computational Complexity
  • Computational Science
  • Data Association
  • Gaussian Processes
  • Information Science
  • Measurement
  • Multiple Hypothesis Tracking
  • Multitarget Tracking
  • Probabilistic Models
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • Target Tracking
  • Warfare

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.