Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation
The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stabil...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2072-4292/16/23/4384 |
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| author | Shoupeng Li Weiwei Liu |
| author_facet | Shoupeng Li Weiwei Liu |
| author_sort | Shoupeng Li |
| collection | DOAJ |
| description | The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter. |
| format | Article |
| id | doaj-art-d45f9c991e5e4e7aa149486f9b26a56c |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d45f9c991e5e4e7aa149486f9b26a56c2025-08-20T02:50:40ZengMDPI AGRemote Sensing2072-42922024-11-011623438410.3390/rs16234384Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative NavigationShoupeng Li0Weiwei Liu1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaThe non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter.https://www.mdpi.com/2072-4292/16/23/4384cost functionlocal minimumnon-Gaussian noiseM-estimationKalman filteriterative methods |
| spellingShingle | Shoupeng Li Weiwei Liu Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation Remote Sensing cost function local minimum non-Gaussian noise M-estimation Kalman filter iterative methods |
| title | Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation |
| title_full | Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation |
| title_fullStr | Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation |
| title_full_unstemmed | Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation |
| title_short | Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation |
| title_sort | sequential mixed cost based multi sensor and relative dynamics robust fusion for spacecraft relative navigation |
| topic | cost function local minimum non-Gaussian noise M-estimation Kalman filter iterative methods |
| url | https://www.mdpi.com/2072-4292/16/23/4384 |
| work_keys_str_mv | AT shoupengli sequentialmixedcostbasedmultisensorandrelativedynamicsrobustfusionforspacecraftrelativenavigation AT weiweiliu sequentialmixedcostbasedmultisensorandrelativedynamicsrobustfusionforspacecraftrelativenavigation |