Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter
This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis di...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/2/488 |
| _version_ | 1818038028282626048 |
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| author | Bingbing Gao Gaoge Hu Shesheng Gao Yongmin Zhong Chengfan Gu |
| author_facet | Bingbing Gao Gaoge Hu Shesheng Gao Yongmin Zhong Chengfan Gu |
| author_sort | Bingbing Gao |
| collection | DOAJ |
| description | This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation. |
| first_indexed | 2024-12-10T07:36:13Z |
| format | Article |
| id | doaj.art-4ee3eff7b31444beaaf882acd701edef |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:36:13Z |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4ee3eff7b31444beaaf882acd701edef2022-12-22T01:57:25ZengMDPI AGSensors1424-82202018-02-0118248810.3390/s18020488s18020488Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman FilterBingbing Gao0Gaoge Hu1Shesheng Gao2Yongmin Zhong3Chengfan Gu4School of Automatics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automatics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Automatics, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Engineering, RMIT University, Bundoora, VIC 3083, AustraliaSchool of Engineering, RMIT University, Bundoora, VIC 3083, AustraliaThis paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.http://www.mdpi.com/1424-8220/18/2/488multi-sensor data fusionadaptive fading unscented Kalman filterprocess-modeling errorMahalanobis distancelinear minimum variance |
| spellingShingle | Bingbing Gao Gaoge Hu Shesheng Gao Yongmin Zhong Chengfan Gu Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter Sensors multi-sensor data fusion adaptive fading unscented Kalman filter process-modeling error Mahalanobis distance linear minimum variance |
| title | Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter |
| title_full | Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter |
| title_fullStr | Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter |
| title_full_unstemmed | Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter |
| title_short | Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter |
| title_sort | multi sensor optimal data fusion based on the adaptive fading unscented kalman filter |
| topic | multi-sensor data fusion adaptive fading unscented Kalman filter process-modeling error Mahalanobis distance linear minimum variance |
| url | http://www.mdpi.com/1424-8220/18/2/488 |
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