Abstract
A time-varying Kalman filter is proposed to solve the problem of remote estimation with sensor scheduling and measurement loss. The statistical properties of the estimation error are studied. The expectation of the estimation error covariance is proved to have upper and lower bounds. Convergence conditions and methods to calculate these bounds are also presented. The optimal sensor selection probability is found by using gradient search method. When the remote estimator schedules the transmission of sensors using optimal probability, the best estimation performance can be obtained. The validity of the proposed results are demonstrated by numerical examples.
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Xiao, L., Sun, Z., Zhu, D., Chen, M. (2009). Remote Estimation with Sensor Scheduling. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_89
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DOI: https://doi.org/10.1007/978-3-642-01513-7_89
Publisher Name: Springer, Berlin, Heidelberg
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