Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning
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| Publicado no: | Remote Sensing vol. 17, no. 2 (2025), p. 207 |
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| Autor principal: | |
| Outros Autores: | , , , , , |
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/rs17020207 |2 doi | |
| 035 | |a 3159535639 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Wang, Sen |u School of Integrated Circuits, Shandong University, Jinan 250101, China; <email>202120349@mail.sdu.edu.cn</email> (S.W.); <email>xingjp@sdu.edu.cn</email> (J.X.) | |
| 245 | 1 | |a Maximum Mixture Correntropy Criterion-Based Variational Bayesian Adaptive Kalman Filter for INS/UWB/GNSS-RTK Integrated Positioning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Wireless communications | ||
| 653 | |a Navigation systems | ||
| 653 | |a Accuracy | ||
| 653 | |a State estimation | ||
| 653 | |a Drift estimation | ||
| 653 | |a Error analysis | ||
| 653 | |a Inertial coordinates | ||
| 653 | |a Kalman filters | ||
| 653 | |a Inertial navigation | ||
| 653 | |a Position measurement | ||
| 653 | |a Ultrawideband | ||
| 653 | |a Robust control | ||
| 653 | |a Adaptive systems | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Disturbances | ||
| 653 | |a Noise control | ||
| 653 | |a Sensors | ||
| 653 | |a Criteria | ||
| 653 | |a Random noise | ||
| 653 | |a Unmanned ground vehicles | ||
| 653 | |a Algorithms | ||
| 653 | |a Mixtures | ||
| 653 | |a Drift | ||
| 653 | |a Inertial platforms | ||
| 653 | |a Inertial sensing devices | ||
| 653 | |a Global navigation satellite system | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Dai, Peipei |u School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, China; <email>daipeipei@sdut.edu.cn</email> | |
| 700 | 1 | |a Xu, Tianhe |u Institute of Space Sciences, Shandong University, Weihai 264209, China; <email>wenfengnie@sdu.edu.cn</email> (W.N.); <email>yzcong@sdu.edu.cn</email> (Y.C.); <email>gaofan@sdu.edu.cn</email> (F.G.) | |
| 700 | 1 | |a Nie, Wenfeng |u Institute of Space Sciences, Shandong University, Weihai 264209, China; <email>wenfengnie@sdu.edu.cn</email> (W.N.); <email>yzcong@sdu.edu.cn</email> (Y.C.); <email>gaofan@sdu.edu.cn</email> (F.G.) | |
| 700 | 1 | |a Cong, Yangzi |u Institute of Space Sciences, Shandong University, Weihai 264209, China; <email>wenfengnie@sdu.edu.cn</email> (W.N.); <email>yzcong@sdu.edu.cn</email> (Y.C.); <email>gaofan@sdu.edu.cn</email> (F.G.) | |
| 700 | 1 | |a Xing, Jianping |u School of Integrated Circuits, Shandong University, Jinan 250101, China; <email>202120349@mail.sdu.edu.cn</email> (S.W.); <email>xingjp@sdu.edu.cn</email> (J.X.) | |
| 700 | 1 | |a Gao, Fan |u Institute of Space Sciences, Shandong University, Weihai 264209, China; <email>wenfengnie@sdu.edu.cn</email> (W.N.); <email>yzcong@sdu.edu.cn</email> (Y.C.); <email>gaofan@sdu.edu.cn</email> (F.G.) | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 2 (2025), p. 207 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159535639/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159535639/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159535639/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |