| Resumen |
This paper presents two stochastic filters considering autoregressive models of first and second order for parameter estimation and system identification. Each model is applied to a reference of the corresponding order and their recursive and non-recursive estimation results are compared; obtaining their error functional values to determine their performance. Due to the recursive methods give better approximation results, than the non-recursive ones, they are applied to describe the behaviour of the wind, which is a stochastic signal useful in the aerodynamic field, comparing the tracking results through off the functional error and the surroundings of the relative frequency histograms; including also a computational complexity graphic. To conclude, the second order filter has a better convergence performance at the expense of a higher computational cost, its pros and cons are mentioned. Nevertheless, choosing the filter order depends on its application. |
