Accuracy and consistency in estimation and fusion over long-haul sensor networks

Qiang Liu, Xin Wang, Nageswara S.V. Rao

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

Abstract

Long-haul sensor networks can be found in many real-world applications, such as tracking and/or monitoring of one or more dynamic targets in space. In such networks, sensors are remotely deployed over a large geographical area, whereas a remote fusion center fuses the information provided by these sensors in order to improve the accuracy of the final estimates of certain target characteristics. We consider the accuracy as well as consistency of information measures such as the error covariance matrices used to describe the theoretical error performance of sensor and fuser estimates. In particular, the impact of filtering and fusion, communication loss and delay, sensor bias, and information feedback on the accuracy and consistency of error measures is investigated by means of studying a maneuvering target tracking application.

Original languageEnglish
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages468-475
Number of pages8
ISBN (Electronic)9780982443866
StatePublished - Sep 14 2015
Event18th International Conference on Information Fusion, Fusion 2015 - Washington, United States
Duration: Jul 6 2015Jul 9 2015

Publication series

Name2015 18th International Conference on Information Fusion, Fusion 2015

Conference

Conference18th International Conference on Information Fusion, Fusion 2015
Country/TerritoryUnited States
CityWashington
Period07/6/1507/9/15

Keywords

  • Long-haul sensor networks
  • error covariance matrices
  • estimation bias
  • information consistency
  • information feedback
  • reporting deadline
  • root-mean-square-error (RMSE) performance
  • state estimate fusion

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