Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning
The safe operation of unmanned ground vehicles (UGVs) demands fundamental and essential requirements for continuous and reliable positioning performance. Traditional coupled navigation systems, combining the global navigation satellite system (GNSS) with an inertial navigation system (INS), provide...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/207 |
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| author | Sen Wang Peipei Dai Tianhe Xu Wenfeng Nie Yangzi Cong Jianping Xing Fan Gao |
| author_facet | Sen Wang Peipei Dai Tianhe Xu Wenfeng Nie Yangzi Cong Jianping Xing Fan Gao |
| author_sort | Sen Wang |
| collection | DOAJ |
| description | The safe operation of unmanned ground vehicles (UGVs) demands fundamental and essential requirements for continuous and reliable positioning performance. Traditional coupled navigation systems, combining the global navigation satellite system (GNSS) with an inertial navigation system (INS), provide continuous, drift-free position estimation. However, challenges like GNSS signal interference and blockage in complex scenarios can significantly degrade system performance. Moreover, ultra-wideband (UWB) technology, known for its high precision, is increasingly used as a complementary system to the GNSS. To tackle these challenges, this paper proposes a novel tightly coupled INS/UWB/GNSS-RTK integrated positioning system framework, leveraging a variational Bayesian adaptive Kalman filter based on the maximum mixture correntropy criterion. This framework is introduced to provide a high-precision and robust navigation solution. By incorporating the maximum mixture correntropy criterion, the system effectively mitigates interference from anomalous measurements. Simultaneously, variational Bayesian estimation is employed to adaptively adjust noise statistical characteristics, thereby enhancing the robustness and accuracy of the integrated system’s state estimation. Furthermore, sensor measurements are tightly integrated with the inertial measurement unit (IMU), facilitating precise positioning even in the presence of interference from multiple signal sources. A series of real-world and simulation experiments were carried out on a UGV to assess the proposed approach’s performance. Experimental results demonstrate that the approach provides superior accuracy and stability in integrated system state estimation, significantly mitigating position drift error caused by uncertainty-induced disturbances. In the presence of non-Gaussian noise disturbances introduced by anomalous measurements, the proposed approach effectively implements error control, demonstrating substantial advantages in positioning accuracy and robustness. |
| format | Article |
| id | doaj-art-84ad282f252346c89edd0c95a3d35dca |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-84ad282f252346c89edd0c95a3d35dca2025-01-24T13:47:43ZengMDPI AGRemote Sensing2072-42922025-01-0117220710.3390/rs17020207Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated PositioningSen Wang0Peipei Dai1Tianhe Xu2Wenfeng Nie3Yangzi Cong4Jianping Xing5Fan Gao6School of Integrated Circuits, Shandong University, Jinan 250101, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaSchool of Integrated Circuits, Shandong University, Jinan 250101, ChinaInstitute of Space Sciences, Shandong University, Weihai 264209, ChinaThe safe operation of unmanned ground vehicles (UGVs) demands fundamental and essential requirements for continuous and reliable positioning performance. Traditional coupled navigation systems, combining the global navigation satellite system (GNSS) with an inertial navigation system (INS), provide continuous, drift-free position estimation. However, challenges like GNSS signal interference and blockage in complex scenarios can significantly degrade system performance. Moreover, ultra-wideband (UWB) technology, known for its high precision, is increasingly used as a complementary system to the GNSS. To tackle these challenges, this paper proposes a novel tightly coupled INS/UWB/GNSS-RTK integrated positioning system framework, leveraging a variational Bayesian adaptive Kalman filter based on the maximum mixture correntropy criterion. This framework is introduced to provide a high-precision and robust navigation solution. By incorporating the maximum mixture correntropy criterion, the system effectively mitigates interference from anomalous measurements. Simultaneously, variational Bayesian estimation is employed to adaptively adjust noise statistical characteristics, thereby enhancing the robustness and accuracy of the integrated system’s state estimation. Furthermore, sensor measurements are tightly integrated with the inertial measurement unit (IMU), facilitating precise positioning even in the presence of interference from multiple signal sources. A series of real-world and simulation experiments were carried out on a UGV to assess the proposed approach’s performance. Experimental results demonstrate that the approach provides superior accuracy and stability in integrated system state estimation, significantly mitigating position drift error caused by uncertainty-induced disturbances. In the presence of non-Gaussian noise disturbances introduced by anomalous measurements, the proposed approach effectively implements error control, demonstrating substantial advantages in positioning accuracy and robustness.https://www.mdpi.com/2072-4292/17/2/207maximum mixture correntropy criterionvariational Bayesiantightly coupled integrationinertial navigation system (INS)/ultra-wideband (UWB)/global navigation satellite system real-time kinematic (GNSS-RTK)multi-sensor fusion |
| spellingShingle | Sen Wang Peipei Dai Tianhe Xu Wenfeng Nie Yangzi Cong Jianping Xing Fan Gao Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning Remote Sensing maximum mixture correntropy criterion variational Bayesian tightly coupled integration inertial navigation system (INS)/ultra-wideband (UWB)/global navigation satellite system real-time kinematic (GNSS-RTK) multi-sensor fusion |
| title | Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning |
| title_full | Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning |
| title_fullStr | Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning |
| title_full_unstemmed | Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning |
| title_short | Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning |
| title_sort | maximum mixture correntropy criterion based variational bayesian adaptive kalman filter for ins uwb gnss rtk integrated positioning |
| topic | maximum mixture correntropy criterion variational Bayesian tightly coupled integration inertial navigation system (INS)/ultra-wideband (UWB)/global navigation satellite system real-time kinematic (GNSS-RTK) multi-sensor fusion |
| url | https://www.mdpi.com/2072-4292/17/2/207 |
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