Target location estimation in sensor networks with quantized data

Ruixin Niu, Pramod K. Varshney

Research output: Contribution to journalArticle

201 Scopus citations

Abstract

A signal intensity based maximum-likelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Cramér-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations.

Original languageEnglish (US)
Pages (from-to)4519-4528
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume54
Issue number12
DOIs
StatePublished - Dec 1 2006

Keywords

  • Cramér-rao lower bound
  • Location estimation
  • Quantization
  • Wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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