An Optimal Fusion Method of Multiple Inertial Measurement Units Based on Measurement Noise Variance Estimation

At present, most inertial systems generally only contain a single inertial measurement unit (IMU). Considering the low cost and low accuracy of the micro-electromechanical system (MEMS)-IMU, it has attracted much attention to fuse multiple IMUs to improve the accuracy and robustness of the system. In this article, two online noise variance estimators based on second-order-mutual-difference (SOMD) algorithm are proposed for two redundant measurements and multiple redundant measurements, respectively. In addition, the unbiasedness and consistency of the estimators are proved. Using the proposed noise variance estimators, measurement noise variances of each sensor can be estimated in real time when multiple IMUs exist. Based on the estimated noise variance of each sensor, the weighted least squares (WLS) estimation method is used to generate the optimal virtual IMU (VIMU) in the observation domain. Finally, comparative simulations and the real-world experiment were conducted to evaluate the proposed online noise estimation algorithm. The simulation results demonstrate its superiority compared with other noise variance estimation methods, and the real-world experiment results show the effectiveness of the IMU fusion method based on the proposed noise variance estimation algorithm.