A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages
Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substitut...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/2/256 |
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| author | Wei Fang Jinguang Jiang Shuangqiu Lu Yilin Gong Yifeng Tao Yanan Tang Peihui Yan Haiyong Luo Jingnan Liu |
| author_facet | Wei Fang Jinguang Jiang Shuangqiu Lu Yilin Gong Yifeng Tao Yanan Tang Peihui Yan Haiyong Luo Jingnan Liu |
| author_sort | Wei Fang |
| collection | DOAJ |
| description | Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm. |
| first_indexed | 2024-12-10T20:19:03Z |
| format | Article |
| id | doaj.art-3bd4165e5afc428c947348f3439adc32 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-10T20:19:03Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3bd4165e5afc428c947348f3439adc322022-12-22T01:35:06ZengMDPI AGRemote Sensing2072-42922020-01-0112225610.3390/rs12020256rs12020256A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal OutagesWei Fang0Jinguang Jiang1Shuangqiu Lu2Yilin Gong3Yifeng Tao4Yanan Tang5Peihui Yan6Haiyong Luo7Jingnan Liu8GNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaSchool of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaInstitute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaAiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.https://www.mdpi.com/2072-4292/12/2/256lstmins/gnss integrated navigation systemgnss outagepseudo measurement estimating |
| spellingShingle | Wei Fang Jinguang Jiang Shuangqiu Lu Yilin Gong Yifeng Tao Yanan Tang Peihui Yan Haiyong Luo Jingnan Liu A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages Remote Sensing lstm ins/gnss integrated navigation system gnss outage pseudo measurement estimating |
| title | A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages |
| title_full | A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages |
| title_fullStr | A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages |
| title_full_unstemmed | A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages |
| title_short | A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages |
| title_sort | lstm algorithm estimating pseudo measurements for aiding ins during gnss signal outages |
| topic | lstm ins/gnss integrated navigation system gnss outage pseudo measurement estimating |
| url | https://www.mdpi.com/2072-4292/12/2/256 |
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