An Estimation-Domain Approach to MEMS Multi-IMU Fusion for sUAS

Small unmanned aerial systems (sUAS) require precise inertial state information at high temporal rates for the purposes of stability and navigation as well as to provide accurate attitude estimates to scientific payloads. Common sUAS inertial navigation systems implement a single strapdown inertial measurement unit (IMU), typically made up of an orthogonal triad of linear accelerometers and a similar triad of rate gyroscopes alongside a single GPS/GNSS receiver to accomplish this task. These outputs are fused optimally, sometimes with other sensors, in an extended Kalman filter (EKF) architecture to generate reliable estimates of the vehicle states. This work develops an estimation-domain fusion strategy for combining the state estimates from multiple inertial navigation systems (INS) for the purposes of sUAS navigation and imaging. The algorithm is implemented in MATLAB and the results are presented.