Abstract
Bayes 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. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. 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. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.
Highlights
Bayes 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
In the deep extended Kalman filter, Inertial Measurement Unit (IMU) errors were modelled in addition to the prediction and update stages
Extended Kalman filter, IMU errors were modelled in addition to the prediction and update stages
Summary
Bayes 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. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. Global Navigation Satellite Systems (GNSS) enable us to locate ourselves within a few centimeters all over the world. This system consists of a Global Positioning System (GPS), Galileo, GLobal Orbiting. Other alternative navigation solutions have been applied to overcome this shortcoming of GNSS positioning and bridge its gaps in urban canyons. The Inertial Measurement Unit (IMU) is a composition of accelerometers and gyroscopes and it estimates the position, velocity, and orientation of a platform from measured accelerations and angular rates
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