An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise
Aiming at the problem that the pseudo-velocity measurement noise of non-holonomic constraints (NHCs) in the integrated navigation of vehicle-mounted a global navigation satellite system/inertial navigation system (GNSS/INS) is time-varying and thick-tailed in complex road conditions (turning, sidesl...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1126 |
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| author | Zijian Wang Jianghua Liu Jinguang Jiang Jiaji Wu Qinghai Wang Jingnan Liu |
| author_facet | Zijian Wang Jianghua Liu Jinguang Jiang Jiaji Wu Qinghai Wang Jingnan Liu |
| author_sort | Zijian Wang |
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| description | Aiming at the problem that the pseudo-velocity measurement noise of non-holonomic constraints (NHCs) in the integrated navigation of vehicle-mounted a global navigation satellite system/inertial navigation system (GNSS/INS) is time-varying and thick-tailed in complex road conditions (turning, sideslip, etc.) and cannot be accurately predicted, an adaptive estimation method for the initial value of NHC lateral velocity noise based on multiple linear regression is proposed. On the basis of this method, a Gaussian Student’s T distribution variational Bayesian filtering algorithm (Ga-St VBAKF) based on NHC pseudo-velocity measurement noise modeling is proposed through modeling and analysis of pseudo-velocity measurement noise. Firstly, in order to adaptively adjust the initial value of NHC lateral velocity noise, a vehicle turning detection algorithm is used to detect whether the vehicle is turning. Secondly, based on the vehicle motion state, the variational Bayesian method is used to adaptively estimate the statistical characteristics of the measurement noise in real time based on modeling of the lateral velocity noise as Gaussian white noise or Student’s T distribution thick-tail noise. The test results show that compared to the traditional Kalman filtering algorithm with fixed noise, the Ga-St VBAKF algorithm with noise adaptation reduces the maximum horizontal position error by 65.9% in the GNSS/NHC/OD/INS (where OD stands for odometer and INS stands for inertial measurement unit) system when the vehicle is in a turning state, and by 42.3% in the NHC/OD/INS system. This indicates that the algorithm can effectively suppress the divergence of positioning errors during turning and improve the performance of integrated navigation. |
| format | Article |
| id | doaj-art-3aa2e2edd97b44eeb32b71e0879942d2 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3aa2e2edd97b44eeb32b71e0879942d22025-08-20T03:03:25ZengMDPI AGRemote Sensing2072-42922025-03-01177112610.3390/rs17071126An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement NoiseZijian Wang0Jianghua Liu1Jinguang Jiang2Jiaji Wu3Qinghai Wang4Jingnan Liu5GNSS Research Center, Wuhan University, Wuhan 430079, ChinaSchool of Electronics and Information Engineering, Hubei University of Science and Technology, Xianning 437100, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaElectronic Information School, Wuhan University, Wuhan 430079, ChinaGNSS Research Center, Wuhan University, Wuhan 430079, ChinaAiming at the problem that the pseudo-velocity measurement noise of non-holonomic constraints (NHCs) in the integrated navigation of vehicle-mounted a global navigation satellite system/inertial navigation system (GNSS/INS) is time-varying and thick-tailed in complex road conditions (turning, sideslip, etc.) and cannot be accurately predicted, an adaptive estimation method for the initial value of NHC lateral velocity noise based on multiple linear regression is proposed. On the basis of this method, a Gaussian Student’s T distribution variational Bayesian filtering algorithm (Ga-St VBAKF) based on NHC pseudo-velocity measurement noise modeling is proposed through modeling and analysis of pseudo-velocity measurement noise. Firstly, in order to adaptively adjust the initial value of NHC lateral velocity noise, a vehicle turning detection algorithm is used to detect whether the vehicle is turning. Secondly, based on the vehicle motion state, the variational Bayesian method is used to adaptively estimate the statistical characteristics of the measurement noise in real time based on modeling of the lateral velocity noise as Gaussian white noise or Student’s T distribution thick-tail noise. The test results show that compared to the traditional Kalman filtering algorithm with fixed noise, the Ga-St VBAKF algorithm with noise adaptation reduces the maximum horizontal position error by 65.9% in the GNSS/NHC/OD/INS (where OD stands for odometer and INS stands for inertial measurement unit) system when the vehicle is in a turning state, and by 42.3% in the NHC/OD/INS system. This indicates that the algorithm can effectively suppress the divergence of positioning errors during turning and improve the performance of integrated navigation.https://www.mdpi.com/2072-4292/17/7/1126integrated navigationnon-holonomic constraintsmultiple linear regressionadaptive noisevariational Bayes |
| spellingShingle | Zijian Wang Jianghua Liu Jinguang Jiang Jiaji Wu Qinghai Wang Jingnan Liu An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise Remote Sensing integrated navigation non-holonomic constraints multiple linear regression adaptive noise variational Bayes |
| title | An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise |
| title_full | An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise |
| title_fullStr | An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise |
| title_full_unstemmed | An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise |
| title_short | An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise |
| title_sort | adaptive combined filtering algorithm for non holonomic constraints with time varying and thick tailed measurement noise |
| topic | integrated navigation non-holonomic constraints multiple linear regression adaptive noise variational Bayes |
| url | https://www.mdpi.com/2072-4292/17/7/1126 |
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