Simplicity or flexibility? Complementary Filter vs. EKF for orientation estimation on mobile devices

Contemporary mobile devices can be used as navigation aids. The embedded gyroscope, accelerometer and magnetometer used together may form a reliable AHRS (Attitude and Heading Reference System), which estimates the orientation of the device with respect to the global reference frame. However, a question arises: which framework to use in order to integrate the noisy data under the tight computing power and energy limitations of a mobile device? While the Extended Kalman Filter (EKF) is considered the standard framework to solve estimation problems in navigation, in practice the much simpler Complementary Filter is often applied in systems of limited resources. In this paper we compare the strengths and drawbacks of both frameworks in the application context of Android-based mobile devices. The comparison is focused on the assessment of accuracy and reliability in several real-world motion scenarios.