Approximate state estimation for linear systems with quantized data

Considers the problem of sequential state estimation of discrete-time processes based on quantized measurements. An approximate minimum variance estimator algorithm that recursively updates the state estimate and its error covariance and closely approximates the exact minimum variance estimator is derived. The results of Monte-Carlo simulation are presented and the performance of the algorithm is compared to that of a Kalman filter in which the quantization error is approximated by an additive white Gaussian measurement noise.