Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling
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...
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| Định dạng: | BB |
| Ngôn ngữ: | eng |
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2020
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| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/8487 |
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oai:localhost:DHTL-84872020-05-20T08:23:52Z Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling Hosseinyalamdary S. Deep Kalman filter Simultaneous Sensor Integration and Modelling Recurrent neural network Global Navigation Satellite System Long-Short Term Memory Deep learning 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. https://www.mdpi.com/1424-8220/18/5/1316/htm 2020-05-11T08:54:45Z 2020-05-11T08:54:45Z 2018. 30605s BB Sensors Vol 18, Issue 5, 15 p. 1424-8220 00024827 http://tailieuso.tlu.edu.vn/handle/DHTL/8487 eng null |
| institution |
Trường Đại học Thủy Lợi |
| collection |
DSpace |
| language |
eng |
| topic |
Deep Kalman filter Simultaneous Sensor Integration and Modelling Recurrent neural network Global Navigation Satellite System Long-Short Term Memory Deep learning |
| spellingShingle |
Deep Kalman filter Simultaneous Sensor Integration and Modelling Recurrent neural network Global Navigation Satellite System Long-Short Term Memory Deep learning Hosseinyalamdary S. Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| description |
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. |
| format |
BB |
| author |
Hosseinyalamdary S. |
| author_facet |
Hosseinyalamdary S. |
| author_sort |
Hosseinyalamdary S. |
| title |
Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| title_short |
Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| title_full |
Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| title_fullStr |
Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| title_full_unstemmed |
Deep Kalman FilterSimultaneous Multi-Sensor Integration and Modelling |
| title_sort |
deep kalman filtersimultaneous multi-sensor integration and modelling |
| publisher |
null |
| publishDate |
2020 |
| url |
http://tailieuso.tlu.edu.vn/handle/DHTL/8487 |
| work_keys_str_mv |
AT hosseinyalamdarys deepkalmanfiltersimultaneousmultisensorintegrationandmodelling |
| _version_ |
1787740508240478208 |