Comparing Kalman Filter and Diffuse Kalman Filter on a Gps Signal with Noise

The navigation control of an autonomous vehicle can be determined by the coordinates of a GPS (Global Positioning System) positioning system, angular velocity, and acceleration with an INS (Inertial Navigation System). However, the errors associated with these devices do not allow it to be the only measurement system used in an autonomous vehicle. The need arises to implement tools that determine the system’s state reliably at any instant and perform the necessary control actions to fulfill the trajectory optimally, considering the system’s internal model. Therefore, applying a Diffuse Kalman filter is vital, allowing information integration from GPS and other devices. This work was divided into three essential parts such as the Kalman filter, the fuzzy control, and the simulation of a GPS sensor signal, taking into account that, in this last part, a comparison is made with the behavior of a Diffuse Kalman filter. In general, due to the comparisons of the position estimations in GPS measurements, it is evident that the DKF achieves more efficient reliability values since the position estimation error is reduced.