Sequential Bayesian estimation with censored data

Yujiao Zheng, Ruixin Niu, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

9 Scopus citations


In this paper, a new framework for sequential Bayesian estimation in a sensor network by using both the received data and the information conveyed by missing data due to per-sensor censoring is proposed. In this framework, each local sensor maintains a Kalman Filter (KF) and the Fusion Center (FC) runs a particle filter (PF) to track the system state. Informative measurements are selected by the per-sensor censoring process executed at the sensors at each time. Though the less informative measurements are not sent to the FC, their absence still conveys some information, and the proposed scheme exploits such information from the missing message to achieve better inference performance. Numerical examples are provided to support the theoretical results.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Number of pages4
StatePublished - 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012


Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/TerritoryUnited States
CityAnn Arbor, MI


  • Sensor censoring
  • missing data
  • particle filter
  • sequential Bayesian estimation
  • target tracking
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing


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