![]() ![]() ![]() 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |