Joint unscented Kalman filter for dual estimation in a bifilar pendulum for a small UAV

It has always been difficult to accurately estimate the moment of inertia of an object, e.g. an unmanned aerial vehicle (UAV). Whilst various offline estimation methods exist to allow accurate parametric estimation by minimizing an error cost function, they require large memory consumption, high computational effort, and a long convergence time. The initial estimate's accuracy is also vital in attaining convergence. In this paper, a new real time solution to the model identification problem is provided with the use of a Joint Unscented Kalman Filter for dual estimation. The identification procedures can be easily implemented using a microcontroller, a gyroscope sensor, and a simple bifilar pendulum setup. Accuracy, robustness, and convergence speed are achieved.