Abstract
A Kalman filter is an optimal linear estimator developed in 1960. It is a recursive algorithm, i.e. it uses its output as an input for the next cycle. It allows to effectively estimate the dynamic parameters and predict their future values.
Although huge number of papers have been written on this topic, this paper intends to introduce the processes within a Kalman filter and allow them to be fully understood without the need for a broad mathematical background. Several examples of a Kalman filter based on situations in geodesy, surveying and navigation are solved step by step to explicitly show the performance of this approach to linear estimations.