A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems
Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon syste...
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MDPI AG
2019-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/7/1687 |
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| author | Muhammad Adeel Akram Peilin Liu Muhammad Owais Tahir Waqas Ali Yuze Wang |
| author_facet | Muhammad Adeel Akram Peilin Liu Muhammad Owais Tahir Waqas Ali Yuze Wang |
| author_sort | Muhammad Adeel Akram |
| collection | DOAJ |
| description | Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation. |
| first_indexed | 2024-04-11T12:16:15Z |
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| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:16:15Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-84733a6d22154c69a4d1cbd83306ca462022-12-22T04:24:19ZengMDPI AGSensors1424-82202019-04-01197168710.3390/s19071687s19071687A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear SystemsMuhammad Adeel Akram0Peilin Liu1Muhammad Owais Tahir2Waqas Ali3Yuze Wang4Shanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaShanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaShanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaShanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaShanghai Key Laboratory of Navigation and Location-Based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaConsistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.https://www.mdpi.com/1424-8220/19/7/1687EKF (Extended Kalman Filter)IEKF (Iterative Extended Kalmna Filter)iterative Kalman filterreweighted least squareIRWLS (Iterative Reweighted Least Square)multi-sensor integrationrobust filteringrobust estimationnon-linear system |
| spellingShingle | Muhammad Adeel Akram Peilin Liu Muhammad Owais Tahir Waqas Ali Yuze Wang A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems Sensors EKF (Extended Kalman Filter) IEKF (Iterative Extended Kalmna Filter) iterative Kalman filter reweighted least square IRWLS (Iterative Reweighted Least Square) multi-sensor integration robust filtering robust estimation non-linear system |
| title | A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems |
| title_full | A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems |
| title_fullStr | A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems |
| title_full_unstemmed | A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems |
| title_short | A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems |
| title_sort | state optimization model based on kalman filtering and robust estimation theory for fusion of multi source information in highly non linear systems |
| topic | EKF (Extended Kalman Filter) IEKF (Iterative Extended Kalmna Filter) iterative Kalman filter reweighted least square IRWLS (Iterative Reweighted Least Square) multi-sensor integration robust filtering robust estimation non-linear system |
| url | https://www.mdpi.com/1424-8220/19/7/1687 |
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