A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”
In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results conce...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/12/2019 |
| _version_ | 1828740863023382528 |
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| author | Xiao Zhou Gongliu Yang Qingzhong Cai Jing Wang |
| author_facet | Xiao Zhou Gongliu Yang Qingzhong Cai Jing Wang |
| author_sort | Xiao Zhou |
| collection | DOAJ |
| description | In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method. |
| first_indexed | 2024-04-13T00:48:58Z |
| format | Article |
| id | doaj.art-ab0bc1e846ee4d2daa15f9a865f86da4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T00:48:58Z |
| publishDate | 2016-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab0bc1e846ee4d2daa15f9a865f86da42022-12-22T03:09:55ZengMDPI AGSensors1424-82202016-11-011612201910.3390/s16122019s16122019A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”Xiao Zhou0Gongliu Yang1Qingzhong Cai2Jing Wang3School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaIn recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method.http://www.mdpi.com/1424-8220/16/12/2019gravity compensationerror modellingextreme learning machine (ELM)high precision free-INS |
| spellingShingle | Xiao Zhou Gongliu Yang Qingzhong Cai Jing Wang A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” Sensors gravity compensation error modelling extreme learning machine (ELM) high precision free-INS |
| title | A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” |
| title_full | A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” |
| title_fullStr | A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” |
| title_full_unstemmed | A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” |
| title_short | A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine” |
| title_sort | novel gravity compensation method for high precision free ins based on extreme learning machine |
| topic | gravity compensation error modelling extreme learning machine (ELM) high precision free-INS |
| url | http://www.mdpi.com/1424-8220/16/12/2019 |
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