Authors:
Phuong Pham
and
Sesh Commuri
Affiliation:
The University of Oklahoma, United States
Keyword(s):
Distributed Kalman Filter, Wireless Sensor Networks, and Target Tracking.
Related
Ontology
Subjects/Areas/Topics:
Environmental Monitoring and Control
;
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The tracking of mobile targets using Distributed Kalman Filters in a Wireless Sensor Network (WSN) is addressed in this paper. In contrast to the Kalman Filter implementations reported in the literature, our approach has the Kalman Filter running on only one network node at any given time. The knowledge learned by this node, i.e. the system state and the covariance matrix, is passed on to the subsequent node running the filter. Since a finite subset of the sensor nodes is active at any given time, target tracking can be accomplished using lower power compared to centralized implementations of the Kalman Filter. Numerical simulations demonstrate that the proposed algorithm is robust to measurement noise and changes in the velocity of the target. The results in this paper show that the proposed technique for target tracking will result in significant savings in power consumption and will extend the useful life of the WSN.