Autonomous On-Board Calibration of Attitude Sensors and Gyros

This paper presents the state of the art and future prospects for autonomous real-time on-orbit calibration of gyros and attitude sensors. The current practice in ground-based calibration is presented briefly to contrast it with on-orbit calibration. The technical and economic benefits of on-orbit calibration are discussed. Various algorithms for on-orbit calibration are evaluated, including some that are already operating on board spacecraft. Because Redundant Inertial Measurement Units (RIMUs, which are IMUs that have more than three sense axes) are almost ubiquitous on spacecraft, special attention will be given to calibration of RIMUs. In addition, we discuss autonomous on board calibration and how it may be implemented

Attitude Sensor and Gyro Calibration for Messenger

The Redundant Inertial Measurement Unit Attitude Determination/Calibration (RADICAL(TM)) filter was used to estimate star tracker and gyro calibration parameters using MESSENGER telemetry data from three calibration events. We present an overview of the MESSENGER attitude sensors and their configuration is given, the calibration maneuvers are described, the results are compared with previous calibrations, and variations and trends in the estimated calibration parameters are examined. The warm restart and covariance bump features of the RADICAL(TM) filter were used to estimate calibration parameters from two disjoint telemetry streams. Results show that the calibration parameters converge faster with much less transient variation during convergence than when the filter is cold-started at the start of each telemetry stream

Integration of a Multi-Camera Vision System and Strapdown Inertial Navigation System (SDINS) with a Modified Kalman Filter

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free