  We consider sensor transmission power control for state estimation, using a
Bayesian inference approach. A sensor node sends its local state estimate to a
remote estimator over an unreliable wireless communication channel with random
data packet drops. As related to packet dropout rate, transmission power is
chosen by the sensor based on the relative importance of the local state
estimate. The proposed power controller is proved to preserve Gaussianity of
local estimate innovation, which enables us to obtain a closed-form solution of
the expected state estimation error covariance. Comparisons with alternative
non data-driven controllers demonstrate performance improvement using our
approach.
