Kalman Filtering in Wireless Sensor Networks

Abstract

Challenges associated with the scarcity of bandwidth and power in wireless communications have to be addressed. For the state-estimation problems discussed in the paper, observations about a common state are collected by physically distributed terminals. To perform state estimation, wireless sensor networks (WSNs) may share these observations with each other or communicate them to a fusion center for centralized processing. With K vector observations {y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (n)} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k=1</sub> available, the optimal mean squared error (MSE) estimation of the state x(n) for the linear model is accomplished by a Kalman filter.