SENSOR FUSION FOR DISTRIBUTED INERTIAL MEASUREMENT UNITS

Su-Yeon Choi, Holly Dinkel, David Hanley, Tim Bretl

Abstract: This work demonstrates the reduction of Angle Random Walk (ARW), Velocity Random Walk (VRW), and Rate Random Walk (RRW) through sensor fusion of complementary distributed Inertial Measurement Units (IMUs) for robot and satellite navigation. High-fidelity IMUs may violate the tight cost, mass, power, and volume constraints typical of small robot and satellite platforms. Low-cost Micro-Electromechanical System (MEMS) IMUs may have performance characteristics unsuitable for attitude estimation when used alone in a system. In this work, the measurements of multiple MEMS IMUs are fused into a virtual IMU. The fusion algorithm takes time-synchronized measurements from an arbitrary number of sensors and produce one virtual measurement estimate. Allan variance is used to evaluate ARW, VRW, and RRW noise performance for the estimated angular velocity and linear acceleration measurements. The noise performance of the virtual sensor measurement estimates and of the single-sensor measurements is compared. Sensor fusion is demonstrated with the simulated data of up to 18 MEMS IMUs and for real data collected by 5 MEMS IMUs. A tradeoff between number of sensors and noise reduction performance is presented to aid the design of future scalable, distributed inertial measurement systems.