Experimental Analysis of Filtering Algorithms for IMU-Based Applications under Vibrations

The use of microelectro-mechanical systems (MEMS)-based inertial measurement units (IMUs) is widespread in many applications concerning monitoring, diagnostic, and/or controlling in navigation and transportation systems, as well as in low-cost applications for automotive and aeronautical fields. The data provided by the set of sensors typically present in IMUs, as accelerometers, gyroscopes, and magnetometers, are often used also for feeding suitable filtering and positioning algorithms able to correct the attitude and path of the vehicle on which they are installed or to provide the analytical redundancy needed for online diagnosis. Nevertheless, on one hand, the performance of low-cost MEMS-based IMUs is certified only under a small set of nominal operating conditions, and on the other hand, the filtering algorithms are often designed and verified under canonical additive noises. In this framework, this article proposes a test plan and a test setup for analyzing and characterizing the performance of filtering algorithms for positioning based on data coming from low-cost IMUs and able to verify systematically the operation of such algorithms under real scenarios. Two kinds of very popular filtering algorithms have been considered, namely, the complementary filter and the attitude and heading reference systems (AHRS) Kalman filter, which belong to two opposite approaches. The experimental results prove how the typical vibrations present in real scenarios can significantly affect the performance of such algorithms.