Inertial measurement unit calibration using Full Information Maximum Likelihood Optimal Filtering

Abstract

The robustness of Full Information Maximum Likelihood Optimal Filtering (FIMLOF) for inertial measurement unit (IMU) calibration in high-g centrifuge environments is considered. FIMLOF uses an approximate Newton's Method to identify Kalman Filter parameters such as process and measurement noise intensities. Normally, IMU process noise intensities and measurement standard deviations are determined by laboratory testing in a 1-g field. In this thesis, they are identified along with the calibration of the IMU during centrifuge testing. The partial derivatives of the Kalman Filter equations necessary to identify these parameters are developed. Using synthetic measurements, the sensitivity of FIMLOF to initial parameter estimates and filter suboptimality is investigated. The filter residuals, the FIMLOF parameters, and their associated statistics are examined. The results show that FIMLOF can be very successful at tuning suboptimal filter models. For systems with significant mismodeling, FIMLOF can substantially improve the IMU calibration and subsequent navigation performance. In addition, FIMLOF can be used to detect mismodeling in a system, through disparities between the laboratory-derived parameter estimates and the FIMLOF parameter estimates.