Abstract
Remote state estimation in networked control systems always consumes too much sensor battery power and communication bandwidth. Under power and communication constraint, we seek a desirable tradeoff between communication rate and estimation performance in terms of estimation error covariance. We propose two data-driven sensor scheduling strategies to achieve that goal. We prove that under our strategies the minimum mean squared error (MMSE) estimator is a Kalmanlike filter which maintains linearity. We give the explicit MMSE estimator under each strategy. In the end we conduct numerical experiment to show the superiority of our design.
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