Stability and Performance of Wireless Sensor Networks during the Tracking of Dynamic Targets

Abstract

The performance of Wireless Sensor Networks (WSNs) during the tracking of dynamic targets is addressed in this paper. The strategy outlined in this paper uses a Distributed implementation of a Kalman Filter to track dynamic targets. In contrast to the results reported in the literature, the approach in this paper 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. The tracking problem in WSNs is formulated mathematically and the stability and tracking error of the proposed strategy is rigorously analyzed. Numerical simulations are then used to demonstrate the utility of the proposed technique. 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.