Channel aware target localization with quantized data in wireless sensor networks

Onur Ozdemir, Ruixin Niu, Pramod K. Varshney

Research output: Contribution to journalArticle

87 Scopus citations

Abstract

In this paper, we propose a new maximum-likelihood (ML) target localization approach which uses quantized sensor data as well as wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that statistics of imperfect wireless channels between sensors and the fusion center along with some physical layer design parameters are incorporated in the localization algorithm. We call this approach "channel-aware target localization."ML target location estimators are derived for different wireless channel models and receiver architectures. Furthermore, we derive the Cramér-Rao lower bounds (CRLBs) for our proposed channel-aware ML location estimators. Simulation results are presented to show that the performance of the channel-aware ML location estimators are quite close to their theoretical performance bounds even with relatively small number of sensors and their performance is superior compared to that of the channel-unaware ML estimators.

Original languageEnglish (US)
Pages (from-to)1190-1202
Number of pages13
JournalIEEE Transactions on Signal Processing
Volume57
Issue number3
DOIs
StatePublished - Mar 10 2009

Keywords

  • Cramér-Rao lower bound
  • Imperfect communication channels
  • Target localization
  • Wireless sensor networks (WSNs)

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Channel aware target localization with quantized data in wireless sensor networks'. Together they form a unique fingerprint.

  • Cite this