Preprint Article Version 1 This version is not peer-reviewed

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study

Version 1 : Received: 15 March 2018 / Approved: 15 March 2018 / Online: 15 March 2018 (07:10:32 CET)

A peer-reviewed article of this Preprint also exists.

Hosseinyalamdary, S. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors 2018, 18, 1316. Hosseinyalamdary, S. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors 2018, 18, 1316.

Journal reference: Sensors 2018, 18, 1316
DOI: 10.3390/s18051316

Abstract

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

Subject Areas

deep Kalman filter; simultaneous sensor integration and modelling (SSIM); GNSS/IMU integration; recurrent neural network; deep learning; long-short term memory (LSTM)

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