The Star Tracker Accuracy (STAcc) software was developed to rapidly assess the capabilities of star tracker and IMU configurations. The preliminary concept to achieve this precision attitude knowledge is to use star trackers combined with an IMU.
Precision attitude knowledge is essential to the iROC mission to enable high-speed steering of the optical link. iROC is investigating the use of beaconless precision laser pointing to enable laser communication at Mars orbits and beyond. Typical laser communication systems, such as the Lunar Laser Communication Demonstration (LLCD) and the Laser Communication Relay Demonstration (LCRD), use a beacon to locate ground stations. Swank, Aaron J.Ī software tool for estimating cross-boresight error of a star tracker combined with an inertial measurement unit ( IMU) was developed to support trade studies for the Integrated Radio and Optical Communication project (iROC) at the National Aeronautics and Space Administration Glenn Research Center. Star Tracker Performance Estimate with IMUĪretskin-Hariton, Eliot D. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration.
In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. Some sensors, such as Inertial Measurement Unit ( IMU), have complicated error sources. The efficient integration of multiple sensors requires deep knowledge of their error sources.
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling A GNSS/ IMU Case Study.īayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns.
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