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

Siavash Hosseinyalamdary (Corresponding Author)

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)
57 Downloads (Pure)

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.
Original languageEnglish
Article number1316
Number of pages15
JournalSensors (Switserland)
Volume18
Issue number5
Early online date24 Apr 2018
DOIs
Publication statusPublished - 2018

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

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