A robust estimation scheme for clock phase offsets in wireless sensor networks in the presence of non-Gaussian random delays

Jang Sub Kim, Jaehan Lee, Erchin Serpedin, Khalid Qaraqe

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

To cope with the Gaussian or non-Gaussian nature of the random network delays, a novel method, referred to as the Gaussian mixture Kalman particle filter (GMKPF), is proposed herein to estimate the clock offset in wireless sensor networks. GMKPF represents a better and more flexible alternative to the symmetric Gaussian maximum likelihood (SGML), and symmetric exponential maximum likelihood (SEML) estimators for clock offset estimation in non-Gaussian or non-exponential random delay models. The computer simulations illustrate that GMKPF yields much more accurate results relative to SGML and SEML when the network delays are modeled in terms of a single non-Gaussian/non-exponential distribution or as a mixture of several distributions.

Original languageEnglish
Pages (from-to)1155-1161
Number of pages7
JournalSignal Processing
Volume89
Issue number6
DOIs
Publication statusPublished - Jun 2009
Externally publishedYes

Fingerprint

Maximum likelihood
Clocks
Wireless sensor networks
Computer simulation

Keywords

  • Clock
  • Estimation
  • Kalman filter
  • Synchronization
  • Wireless networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

A robust estimation scheme for clock phase offsets in wireless sensor networks in the presence of non-Gaussian random delays. / Kim, Jang Sub; Lee, Jaehan; Serpedin, Erchin; Qaraqe, Khalid.

In: Signal Processing, Vol. 89, No. 6, 06.2009, p. 1155-1161.

Research output: Contribution to journalArticle

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