Evaluation and Extensions of the Probabilistic Multi-Hypothesis Tracking Algorithm to Cluttered Environments

Abstract

This research examines the probabilistic multi-hypothesis tracker (PHMT), a batch mode, empirical, Bayesian data association and tracking algorithm. Like a traditional multi-hypothesis tracker (MHT), track estimation is deferred until more conclusive data is gathered. However, unlike a traditional algorithm, PMHT does not attempt to enumerate all possible combinations of feasible data association links, but uses a probabilistic structure derived using expectation maximization. This study focuses on two issues: the behavior of the PMHT algorithm in clutter and algorithm initialization in clutter. We also compare performance between this algorithm and other algorithms, including a nearest neighbor tracker, a probabilistic data association filter (PDAF), and a traditional measurement oriented MHT algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA355908

Entities

People

  • D. T. Dunham
  • R. G. Hutchins

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Batch Processing
  • Data Association
  • Data Processing
  • Environment
  • Equations Of State
  • Gaussian Noise
  • Geometry
  • Information Processing
  • Measurement
  • Multiple Hypothesis Tracking
  • Multitarget Tracking
  • Probability
  • Simulations
  • Statistical Algorithms
  • Test And Evaluation
  • Undersea Warfare

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms