Continuous-time system identification using Gaussian modulating filters

An approach to identification of linear continuous-time system is considered with modulating functions. Modulating functions are constructed by dilating Gaussian base function, and the correspondent filters have been analyzed. Using linear modulating filters, an identification model that is parameterized directly in continuous-time model parameters is obtained. Applying the results from discrete-time model identification to the equivalent identification model, the LS estimation algorithms are developed. The relationship between the accuracy of identification and the parameter of modulating filter is investigated, and some tips about designing Gaussian modulating function are outlined. Finally, a simulation study is also included to confirm the theoretical results.