Scheduling multiple sensors using particle filters in target tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations


A critical component of a multi-sensor system is sensor scheduling to optimize system performance under constraints (e.g. power, bandwidth, and computation). In this paper, we apply particle filter sequential Monte Carlo methods to implement multiple sensor scheduling for target tracking. Under the constraint that only one sensor can be used at each time step, we select a sequence of sensor uses to minimize the predicted mean-square error in the target state estimate; the predicted mean-square error is approximated using the particle filter in conjunction with an extended Kaiman filter approximation. Using Monte Carlo simulations, we demonstrate the improved performance of our scheduling approach over the non-scheduling case.

Original languageEnglish (US)
Title of host publicationProceedings of the 2003 IEEE Workshop on Statistical Signal Processing, SSP 2003
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)0780379977
StatePublished - 2003
EventIEEE Workshop on Statistical Signal Processing, SSP 2003 - St. Louis, United States
Duration: Sep 28 2003Oct 1 2003

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings


OtherIEEE Workshop on Statistical Signal Processing, SSP 2003
Country/TerritoryUnited States
CitySt. Louis


  • Cost function
  • Infrared sensors
  • Particle filters
  • Particle measurements
  • Processor scheduling
  • Radar measurements
  • Radar tracking
  • Sensor systems
  • State estimation
  • Target tracking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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