Title :
Filtering Quantized Measurements
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Tech. Univ. Munchen, Muenchen, Germany
Abstract :
The problem of estimating the states of a linear system with quantized measurements is investigated. By introducing the concept of the so-called grid function, it is shown that the quantization effects may be interpreted as a modification of the nonquantized Bayesian posterior distribution. A new filter is obtained by approximating the grid function with a certain class of functions. An explicit filter representation resembling Gaussian sum filters is derived. It is shown that the grid function may also serve as a tool for characterizing well-known filters. We conclude by comparing the filter structure and performance with these well-known filters.
Keywords :
Bayes methods; filtering theory; linear systems; nonlinear filters; quantisation (signal); state estimation; Gaussian sum filters; filter representation; grid function; linear system; nonquantized Bayesian posterior distribution; quantization effects; quantized measurement filtering; state estimation problem; Approximation methods; Equations; Kalman filters; Mathematical model; Noise measurement; Standards; Upper bound; Bayesian methods; filtering algorithms; grid function; nonlinear filters; state estimation;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2012.2212615