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Article

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

Department of Earth Observation Science (EOS), Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, Enschede 7514AE, The Netherlands
Sensors 2018, 18(5), 1316; https://doi.org/10.3390/s18051316
Received: 15 March 2018 / Revised: 11 April 2018 / Accepted: 16 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Smart Vehicular Mobile Sensing)
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. View Full-Text
Keywords: deep Kalman filter; Simultaneous Sensor Integration and Modelling (SSIM); GNSS/IMU integration; recurrent neural network (RNN); deep learning; Long-Short Term Memory (LSTM) deep Kalman filter; Simultaneous Sensor Integration and Modelling (SSIM); GNSS/IMU integration; recurrent neural network (RNN); deep learning; Long-Short Term Memory (LSTM)
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MDPI and ACS Style

Hosseinyalamdary, S. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors 2018, 18, 1316. https://doi.org/10.3390/s18051316

AMA Style

Hosseinyalamdary S. Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study. Sensors. 2018; 18(5):1316. https://doi.org/10.3390/s18051316

Chicago/Turabian Style

Hosseinyalamdary, Siavash. 2018. "Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study" Sensors 18, no. 5: 1316. https://doi.org/10.3390/s18051316

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