n46098SE Academic Article uri icon

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. 2009 Elsevier B.V. All rights reserved.

published proceedings

  • Signal Processing

author list (cited authors)

  • Kim, J., Lee, J., Serpedin, E., & Qaraqe, K.

publication date

  • January 1, 2009 11:11 AM