Applied Sensor Fusion: Tuning Parameters of CF and KF by Means of Evolution Strategies

From the point of view of metrology, inertial sensors acting separately present undesirable performance in the measurement of angular position. In order to provide measurements with greater precision and accuracy, the measures of each of these sensors are typically fused by means of filters. The performance parameters of these filters are hard to tune and several works have been using exhaustive search algorithm or manual experimental tests to tuning these parameters. However, the exhaustive search algorithm usually requires a large computational effort and adjusting parameters manually does not guarantee that the estimated parameters are optimized. In this work, it has been proposed the tuning of the Complementary Filter (CF) and the Kalman Filter (KF) through the heuristic method Evolutionary Strategies. Experimental results have shown that our method is a useful tool that considerably reduces the time to find the tuning of the FC and the FK. In addition, the use of the tuned FC and FK improved significantly metrological characteristics of the system. The use of Bland and Altman's statistical method show that the measurements of the angular position have a good agreement with the actual angular position of a servo motor.