Applications of bayesian filtering in wireless networks: clock synchronization, localization, and rf tomography

Ad hoc networks;Distributed;Estimation;Tracking;Imaging;Time synchronization

Hamilton, Benjamin Russell ; Electrical and Computer Engineering

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Ad hoc networks;    Distributed;    Estimation;    Tracking;    Imaging;    Time synchronization;   

Others  :  https://smartech.gatech.edu/bitstream/1853/44707/1/hamilton_benjamin_r_201208_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a new paradigm to sense the network environment and could greatly enhance existing network capabilities. While there are some existing works addressing these problems, they all suffer from limitations. Current time synchronization methods are not energy efficient on small wireless devices with low quality oscillators. Existing localization methods do not consider additional sources of information available to nodes in the network such as measurements from accelerometers or models of the shadowing environment in the network. RF tomography has only been examined briefly in such networks, and current algorithms can not handle node mobility and rely on shadowing models that have not been experimentally verified. We address the time synchronization problem by analyzing the characteristics of the clocks in small wireless devices, developing a model for it, and then applying a Kalman filter to track both clock offset and skew. In our investigation into RF tomography, we present a method using a Kalman filter which jointly estimates and tracks static and dynamic objects in the environment. We also use channel measurements collected from a field test of our RF tomography testbed to compare RF shadowing models. For the localization problem, we present two algorithms incorporating additional information for improved localization: one based on a distributed extended Kalman filter that combines local acceleration measurements with signal strength measurements for improved localization, and another that uses a distributed particle filter to incorporate a model of the channel environment.