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

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.