Stochastic System Model Evaluated with First and Second Order Filters

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.