Advancements of Particle Filtering Theory and it's Application to Tracking

Abstract

The main goal of this work is the development of a general class of particle filtering methods and apply them to various problems related to target tracking. Theoretically, the project involves the advancement of existing particle filtering schemes and the development of new ones that relax the probabilistic assumptions of the standard methods. The design of the new filters is guided by the objectives of securing (a) excellent performance in target tracking in most demanding situations, (b) robustness, and (c) relatively easy hardware implementation. Practical efforts include applications of the filters to tracking of single targets as well as to much more challenging tasks such as tracking of multiple targets where the number of targets may vary with time. Finally, scenarios that require multisensor tracking and data fusion are also of interest.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA458305

Entities

People

  • Monica F. Bugallo
  • Peter M. Djuric

Organizations

  • Stony Brook University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Data Fusion
  • Detectors
  • Digital Signal Processing
  • Filters
  • Filtration
  • Kalman Filters
  • Monte Carlo Method
  • Multiple Targets
  • Multitarget Tracking
  • Networks
  • Sensor Networks
  • Sequential Monte Carlo Methods
  • Signal Processing
  • Target Tracking
  • Targets

Fields of Study

  • Engineering

Readers

  • Aerosol Science/Aerosol Physics
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design