Abstract
Two Kalman Filter (KF) like algorithms for state estimation of linear systems from multilevel quantized outputs are introduced in this paper. The proposed algorithms are proved to be optimal in the sense that they minimize the a posteriori state estimation error covariance matrix. Monte Carlo simulations are carried out to illustrate their performance for different quantization steps and Signal-to-Noise Ratios. It is also shown how the algorithms can be employed for the parameter estimation of linear systems in linear regression form, from quantized outputs.
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