An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area
In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/4/612 |
| _version_ | 1797297089200259072 |
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| author | Zhaoxu Tian Yongmei Cheng Shun Yao Zhenwei Li |
| author_facet | Zhaoxu Tian Yongmei Cheng Shun Yao Zhenwei Li |
| author_sort | Zhaoxu Tian |
| collection | DOAJ |
| description | In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation. |
| first_indexed | 2024-03-07T22:16:13Z |
| format | Article |
| id | doaj.art-ad94a1f32a784677a561b73450a914dd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-07T22:16:13Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ad94a1f32a784677a561b73450a914dd2024-02-23T15:32:53ZengMDPI AGRemote Sensing2072-42922024-02-0116461210.3390/rs16040612An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea AreaZhaoxu Tian0Yongmei Cheng1Shun Yao2Zhenwei Li3School of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaIn this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.https://www.mdpi.com/2072-4292/16/4/612integrated navigationcloud and fog modelsea area landmarksautomatic classification modelusability evaluation |
| spellingShingle | Zhaoxu Tian Yongmei Cheng Shun Yao Zhenwei Li An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area Remote Sensing integrated navigation cloud and fog model sea area landmarks automatic classification model usability evaluation |
| title | An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area |
| title_full | An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area |
| title_fullStr | An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area |
| title_full_unstemmed | An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area |
| title_short | An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area |
| title_sort | adaptive ins cns smn integrated navigation algorithm in sea area |
| topic | integrated navigation cloud and fog model sea area landmarks automatic classification model usability evaluation |
| url | https://www.mdpi.com/2072-4292/16/4/612 |
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