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

• Published in: Signal processing Vol. 89; no. 6; pp. 1155 - 1161
• Main Authors: Kim, Jang-Sub, Lee, Jaehan, Serpedin, Erchin, Qaraqe, Khalid
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-06-2009; Elsevier
• Subjects: Applied sciences; Clock; Clocks; Delay; Detection, estimation, filtering, equalization, prediction; Estimation; Exact sciences and technology; Gaussian; Information, signal and communications theory; Kalman filter; Mathematical models; Networks; Non-Gaussian; Offsets; Sensors; Signal and communications theory; Signal, noise; Synchronization; Telecommunications and information theory; Wireless networks; Wireless networks; Clock; Synchronization; Estimation; Kalman filter; Exponential distribution; Computer simulation; Wireless telecommunication; Gaussian distribution; Robust estimation; Remote sensing; SGML language; Gaussian process; Wireless network; Delay time; Maximum likelihood; Sensor array; Signal detection; Particle filter
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2008.12.021

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.