Sensor Fusion To Improve State Estimate Accuracy Using Multiple Inertial Measurement Units

The growing availability of low-cost commercial inertial measurement units (IMUs) raises questions about how to best improve sensor estimates when using multiple IMUs. This paper reports on the performance of two approaches applied to GPS-denied onboard attitude estimation. The approaches are a virtual IMU approach fusing sensor measurements and a Federated Filter fusing state estimates from several Extended Kalman Filters (EKFs) each using one IMU and magnetometer. We compare their performance as quantified by root mean square (RMS) using parallel implementations of estimators in a Raspberry-Pi-based autopilot during prescribed motions in a motion capture volume. The results suggest that a Multi-IMU GPS-denied approach can deliver comparable performance to the single-IMU GPS aided approach and provide a testbed for multi-IMU performance quantification.
^1
1
Portions of this work received support from NASA University Leadership Initiative grant 80NSSC20M0162.