System identification for an unmanned aerial vehicle using the maximum likelihood method

System identification provides one way of efficiently obtaining dynamic models. This paper applies maximum likelihood method for system identification to determine numerical values of model parameters based on measured system outputs. In the paper at hand, the rigid body equations of motion are modeled using the software FALCON.m, which is able to automatically compute the model's analytic derivatives. The validity of the approach is demonstrated by applying it to simulated data. Comparing the estimation results with the original data shows very good agreement, thus proving the applicability of the method in this context.