الفهرس 
A localization technique in mobile wireless sensor network
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Abstract
Localization of moving targets in indoor environment is increasingly becoming an integral part of many emerging business applications. Localization techniques based on received signal strength (RSS) are known to be one of the most common techniques for indoor localization due to their relative low cost as no expensive devices (e.g., GPS) are needed. However, estimation accuracy, using RSS suffers from two shortcomings. First, the intensive human effort is needed in order to prepare the indoor environment for localization with high accuracy. Second, RSS estimation accuracy is notable impacted by the obstacles that natural exist in the real-life indoor environment. Accordingly, in this dissertation, we attempt to address the aforementioned challenges in RSS-based estimation for indoor environments. To this end, the dissertation proposes and evaluates three techniques to estimate the observation model parameters for simulated and real time collected RSS data sets. The fundamental concept of the three techniques is to model the motion dynamics of a mobile target as a hidden Markov model in which each state corresponds to geographic location, then estimate the mobile target location with a position estimator using Bayesian minimum mean square error BMMSE within the fingerprint locations