Designing and Implementing a Standalone MEMS-Based AHRS Algorithm for High-Dynamics Circumstances on navigation systems

In Inertial Navigation Systems (INS), orientation angles are computed via integrating the gyroscopes data. Traditionally, this is done using mechanical gyros of which cost, size, and weight are the limiting factors for their utilization. However, with the advent of Micro-Electro-Mechanical-Systems (MEMS), these limitations have been mitigated significantly, to a degree that most of the modern smart phones today have these sensors onboard. Nevertheless, these sensors provide far less accuracy compared to their mechanical counterparts. Particularly, MEMS gyros collect significant amounts of accumulating error over time, which makes their results unusable after a short period of time. To overcome this problem, data from accelerometers (for Roll and Pitch angles) and magnetometers (for Yaw angle) are also utilized. These external data are fused with that of the gyros in order to control the errors thereof. But the results are vulnerable to the accelerations applied to the platform in the highly dynamic movements. In this research, a new Attitude and Heading Reference System (AHRS) is introduced which uses a complementary filter to fuse the abovementioned approaches based on their characteristics. The results from the field tests (which are conducted via smartphone data) imply that even during the roughest movements, this technique yields an accuracy of about 2 degrees for Roll and Pitch, and 4 degrees for Yaw. These numbers are very promising compared to the traditional approaches that are inferior in all situations, especially under high-dynamic movements.