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Kalman Filtering and Clustering in Sensor Networks

Talebi, Sayed Pouria; Werner, Stefan; Koivunen, Visa
Chapter
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URI
http://hdl.handle.net/11250/2624921
Date
2018
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  • Institutt for elektroniske systemer [1575]
  • Publikasjoner fra CRIStin - NTNU [21809]
Original version
10.1109/ICASSP.2018.8462039
Abstract
In this work, a distributed Kalman filtering and clustering framework for sensor networks tasked with tracking multiple state vector sequences is developed. This is achieved through recursively updating the likelihood of a state vector estimation from one agent offering valid information about the state vector of its neighbors, given the available observation data. These likelihoods then form the diffusion coefficients, used for information fusion over the sensor network. For rigour, the mean and mean square behavior of the developed Kalman filtering and clustering framework is analyzed, convergence criteria are established, and the performance of the developed framework is demonstrated in a simulation example.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)

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