Artificial neural networks for estimation and fusion in long-haul sensor networks

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

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

9 Scopus citations

Abstract

We consider long-haul sensor networks where sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network (ANN) learning-based approaches for estimation and fusion of target states in long-haul sensor networks. The joint effect of (1) imperfect communication condition, namely, link-level loss and delay, and (2) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.

Original languageEnglish
Title of host publication2015 18th International Conference on Information Fusion, Fusion 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-467
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
  • artificial neural networks
  • backpropagation
  • error regularization
  • estimation bias
  • reporting deadline
  • root-mean-square-error (RMSE) performance
  • state estimate fusion

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