Distributed Kalman filtering via node selection in heterogeneous sensor networks

Distributed Kalman filtering via node selection in heterogeneous sensor networks

Donato Di Paola, Antonio Petitti, Alessandro Rizzo ∗ Institute of Intelligent Systems for Automation (ISSIA), National Research Council (CNR), 70126 Bari, Italy; Dipartimento di Ingegneria Elettrica e dell’ Informazione (DEI), Politecnico di Bari, 70126 Bari, Italy Department of Mechanical and Aerospace Engineering, New York University Polytechnic School of Engineering, Six MetroTech Center, Br...

Distributed Kalman Filtering and Sensor Fusion in Sensor Networks

The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using lowpas...

Distributed Kalman Filtering in Sensor Networks with Quantifiable Performance

We analyze the performance of a distributed Kalman filter proposed in recent work on distributed dynamical systems. This approach to distributed estimation is novel in that it admits a systematic analysis of its performance as various network quantities such as connection density, topology, and bandwidth are varied. Our main contribution is a frequency-domain characterization of the distributed...

Collaborative Filtering Based Aggregator Node Selection in Wireless Sensor Networks

In typical WSN, the sensor nodes have limited resources such as battery power, computing capability and memory. In order to achieve the equal usage of limited resources in the wireless sensor networks (WSNs), we must aggregate the sensor data before passing it to the base station. Careful selection of the aggregator nodes in the data aggregation process results in reducing large amounts of comm...

Fault Detection Over Wireless Sensor Networks using Distributed Kalman and Distributed Particle Filtering