Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model
In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error mo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/557 |
| _version_ | 1797437391912304640 |
|---|---|
| author | Zhonghan Li Yongbo Zhang Yutong Shi Shangwu Yuan Shihao Zhu |
| author_facet | Zhonghan Li Yongbo Zhang Yutong Shi Shangwu Yuan Shihao Zhu |
| author_sort | Zhonghan Li |
| collection | DOAJ |
| description | In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error model is proposed, which comprehensively utilizes the mechanism error model and propagation error model. Based on this model, the INS and ultra-wideband (UWB) fusion positioning method is derived relying on the extended Kalman filter (EKF) method. To further improve accuracy, the data prefiltering algorithm of the wavelet shrinkage method based on Stein’s unbiased risk estimate–Shrink (SURE-Shrink) threshold is summarized for raw inertial measurement unit (IMU) data. The experimental results show that by employing the SURE-Shrink wavelet denoising method, positioning accuracy is improved by 76.6%; by applying the two-level error model, the accuracy is further improved by 84.3%. More importantly, at the point when the vehicle motion state changes, adopting the two-level error model can provide higher computational stability and less fluctuation in trajectory curves. |
| first_indexed | 2024-03-09T11:19:33Z |
| format | Article |
| id | doaj.art-cb037ec4e700449d8b5bd25b77233ba9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:19:33Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cb037ec4e700449d8b5bd25b77233ba92023-12-01T00:23:37ZengMDPI AGSensors1424-82202023-01-0123255710.3390/s23020557Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error ModelZhonghan Li0Yongbo Zhang1Yutong Shi2Shangwu Yuan3Shihao Zhu4School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaIn GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve INS accuracy. To this end, a two-level error model is proposed, which comprehensively utilizes the mechanism error model and propagation error model. Based on this model, the INS and ultra-wideband (UWB) fusion positioning method is derived relying on the extended Kalman filter (EKF) method. To further improve accuracy, the data prefiltering algorithm of the wavelet shrinkage method based on Stein’s unbiased risk estimate–Shrink (SURE-Shrink) threshold is summarized for raw inertial measurement unit (IMU) data. The experimental results show that by employing the SURE-Shrink wavelet denoising method, positioning accuracy is improved by 76.6%; by applying the two-level error model, the accuracy is further improved by 84.3%. More importantly, at the point when the vehicle motion state changes, adopting the two-level error model can provide higher computational stability and less fluctuation in trajectory curves.https://www.mdpi.com/1424-8220/23/2/557two-level error modelINSUWBDWTEKFfusion positioning method |
| spellingShingle | Zhonghan Li Yongbo Zhang Yutong Shi Shangwu Yuan Shihao Zhu Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model Sensors two-level error model INS UWB DWT EKF fusion positioning method |
| title | Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model |
| title_full | Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model |
| title_fullStr | Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model |
| title_full_unstemmed | Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model |
| title_short | Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model |
| title_sort | performance enhancement of ins and uwb fusion positioning method based on two level error model |
| topic | two-level error model INS UWB DWT EKF fusion positioning method |
| url | https://www.mdpi.com/1424-8220/23/2/557 |
| work_keys_str_mv | AT zhonghanli performanceenhancementofinsanduwbfusionpositioningmethodbasedontwolevelerrormodel AT yongbozhang performanceenhancementofinsanduwbfusionpositioningmethodbasedontwolevelerrormodel AT yutongshi performanceenhancementofinsanduwbfusionpositioningmethodbasedontwolevelerrormodel AT shangwuyuan performanceenhancementofinsanduwbfusionpositioningmethodbasedontwolevelerrormodel AT shihaozhu performanceenhancementofinsanduwbfusionpositioningmethodbasedontwolevelerrormodel |