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In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and we seek to optimize the a posteriori error covariance over an infinite time horizon. We characterize the exact conditions for the existence of a schedule with uniformly bounded estimation error covariance. Using this result, we construct a scheduling algorithm...
In this paper, we study the problem of using an energy constrained sensor network to estimate the state of a linear dynamical system. The state estimate is computed using a Kalman filter and the goal is to choose a subset of sensors at each time step so as to minimize the a posteriori error covariance. Recent work has indicated that the simple greedy algorithm, which chooses the sensor at each time...
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