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

Authors:
Jang-Sub Kim;Jaehan Lee;Erchin Serpedin;Khalid Qaraqe
Affiliations:
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA;Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA
Venue:
Signal Processing
Year:
2009

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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.