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A new approach to cost-reference particle filtering

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we propose a new cost-reference particle filter that exploits the concept of correlative learning. The objective of applying correlative learning is to obtain the centers of probability distributions that are used for particle generation. Such distributions should provide particles in regions of the state space that have low costs. The new cost-reference particle filter is compared to the original one through computer simulations of a target tracking system that uses range and bearings-only sensors, which are colocated.

Original languageEnglish
Title of host publicationConference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages711-714
Number of pages4
DOIs
StatePublished - 2007
Event41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States
Duration: Nov 4 2007Nov 7 2007

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers

Conference

Conference41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/4/0711/7/07

Keywords

  • Correlative learning
  • Dynamic systems
  • Particle filtering

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