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Accurate, distributed localization and motion analysis algorithms are needed for a variety of mobile wireless sensor network applications. The research on mobile nodes localization and motion analysis in real time will continue to grow as sensor networks are deployed in large numbers and as applications become more varied. In this paper, we introduce a localization and motion analysis parameter estimation algorithm in mobile wireless sensor networks by using pseudo-linear-Kalman filtering, maximum likelihood estimator (MLE) and extended Kalman filter (EKF) techniques. The Cramer-Rao bound (CRB) is also given in this study. Simulations show that the algorithm performs well even with noisy RSS and TOA estimates in the sensors. We apply MLE, EKF and the EKF-based estimator to demonstrate the best bias and variance performance, but the algorithm may not be robust for all random sensor deployments.
Date of Conference: 8-10 Oct. 2006