ABSTRACT
State estimation problems are of great interest in innumerable practical applications. In such kind of problems, the available measured data is used together with prior knowledge about the phenomena of interest and the measurement devices in order to sequentially produce estimates of the desired dynamic variables. This is accomplished in such a manner that the error is statistically minimized. Hence, state estimation problems deal with the combination of model predictions containing uncertainties and measurements that are also intrinsically uncertain in order to obtain more accurate estimations of the system variables. The Kalman filter, as well as a modified version of its recursive procedure, is presented in this chapter.
