On Self-Localization and Tracking with an Unknown Number of Targets

Pranay Sharma, Augustin Alexandru Saucan, Donald J. Bucci, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

4 Scopus citations

Abstract

We propose a scalable algorithm for cooperative self-localization and multi-target tracking. Mobile agents localize themselves, and track an unknown number of targets in the presence of data-association uncertainty. We accomplish this task by modeling the probabilistic relationship between agent and target measurements alongside their state estimates using a suitably designed factor graph, which is solved using loopy belief propagation. By coupling the self-localization and multi-target tracking approaches together, our approach is capable of leveraging the statistical correlation between agent and target state uncertainty in order to provide improved localization accuracy over time for both agents and targets. Simulation results show improvement in target and agent state estimation error, compared to the conventional approach of first localizing agents, and then using this information for target tracking.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1735-1739
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Jul 2 2018
Externally publishedYes
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove
Period10/28/1810/31/18

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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