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Finite-horizon Gaussianity-preserving event-based sensor scheduling in Kalman filter applications. (English) Zbl 1344.93102
Summary: This paper considers a remote state estimation problem, where a sensor measures the state of a linear discrete-time system. The sensor has computational capability to implement a local Kalman filter. The sensor-to-estimator communications are scheduled intentionally over a finite time horizon to obtain a desirable tradeoff between the state estimation quality and the limited communication resources. Compared with the literature, we adopt a Gaussianity-preserving event-based sensor schedule bypassing the nonlinearity problem met in threshold event-based polices. We derive the closed-form of Minimum Mean-Square Error (MMSE) estimator and show that, if communication is triggered, the estimator cannot do better than the local Kalman filter, otherwise, the associated error covariance, is simply a sum of the estimation error of the local Kalman filter and the performance loss due to the absence of communication. We further design the scheduler’s parameters by solving a Dynamic Programming (DP) problem. The computational overhead of the DP problem is less sensitive to the system dimension compared with that of existing algorithms in the literature.

MSC:
93E11 Filtering in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93C05 Linear systems in control theory
93C55 Discrete-time control/observation systems
93C65 Discrete event control/observation systems
90C39 Dynamic programming
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