Online sensor transmission power schedule for remote state estimation

We propose an online sensor transmission power schedule for remote state estimation. A sensor sends its local state estimate to a remote estimator through an unreliable wireless channel, which introduces random data packet drops. The packet dropout rate is related to the transmission power which is allocated by the sensor under an energy constraint. The sensor chooses the transmission power based on the relative importance of the local estimate at each time. We prove that the proposed power schedule preserves the Gaussian distribution of the local estimate innovation, which enable us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative offline schedules are provided, which demonstrate significant performance improvement by the online schedule.