to the location of the unknown nodes. Based on this linearized measurement model, we estimate the location of the unknown nodes using a Kalman filter (KF) instead of a suboptimal extended KF (EKF) and try to estimate the corresponding unknown measurement noise covariance matrix using an iterative process

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. Such distributed Kalman filters typically process the sensor measurements locally at each node rather then communicating them to a single, central node. Characteristic approaches to distributed Kalman filtering are presented in [1]-[4] and the references therein. The proposed methods perform a modified Kalman

Abstract—Tracking a mobile node using a wireless sensor network under non-line of sight (NLOS) conditions, has been considered in this work, which is of interest to indoor positioning applications. A hybrid of time difference of arrival (TDOA) and angle of arrival (AOA) measurements, suitable

This paper proposes a dynamic MMSE estimator for tracking mobile users in indoor Wireless Local Area Networks (WLAN) based on Received Signal Strength (RSS). The method uses a training-based static estimate obtained by an adaptive kernel density estimator as the input into a Kalman Filter

Weproposetwoalgorithmsforanchorlesscooperativenetworklocalizationinmobile wireless sensor networks (WSNs). In order to continuously localize the mobilenetwork, giventhepairwisedistancemeasurementsbetweendifferentwireless sensor nodes, we propose to use subspace tracking to track the variations

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This thesis presents CraneTracker, a multi-modal sensing and communication system for monitoring migratory species at the continental level. By exploiting the robust and extensive cellular infrastructure across the continent, traditional mobile wireless sensor networks can be extended to enable

Abstract—Indoor location tracking of mobile robots or transport vehicles using wireless technology is attractive for many applications. IEEE 802.15.4a wireless networks offer an inexpensive facility for localizing mobile devices by time-based range measurements. The main problems of time

We present Flush, a reliable, high goodput bulk data trans-port protocol for wireless sensor networks. Flush provides end-to-end reliability, reduces transfer time, and adapts to time-varying network conditions. It achieves these proper-ties using end-to-end acknowledgments, implicit snooping