Feature-Aided Random Set Tracking on a Road Constrained Network

Abstract

This paper describes the application of finite set statistics (FISST) to a real-time multiple target road constrained feature-aided tracking problem. A vehicle of interest traverses the road network while other confuser vehicles cross paths with this vehicle. Features extracted from sensors are used to disambiguate the vehicle of interest from the confuser vehicles. The FISST formalism naturally leads to understanding ambiguity in the identity of targets.

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Document Details

Document Type
Technical Report
Publication Date
May 09, 2006
Accession Number
AD1106882

Entities

People

  • David Stein
  • James Witkoskie
  • Michael Otero
  • Stephen Theophanis
  • Walter Kuklinski

Organizations

  • MITRE Corporation

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Detection
  • Detectors
  • Filters
  • Information Science
  • Kalman Filtering
  • Kalman Filters
  • Measurement
  • Multiple Hypothesis Tracking
  • Multiple Targets
  • Multitarget Tracking
  • Probability
  • Probability Distributions
  • Resource Management
  • Sequential Monte Carlo Methods
  • Supervised Machine Learning
  • Target Tracking

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Military Science
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms