Robust Higher Desensitized Cubature Kalman Filter for Uncertain Parameter
| Autor: | Dong-xuan Han, Jiao Yuzhao, Hong-ye Ban, Xiao-qian Wang, Wang Xiaolei, Taishan Lou |
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| Rok vydání: | 2019 |
| Předmět: |
Trace (linear algebra)
Computer simulation Cubature kalman filter 010401 analytical chemistry 02 engineering and technology State (functional analysis) Covariance 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences Matrix (mathematics) Nonlinear system Control theory 0210 nano-technology Mathematics |
| Zdroj: | 2019 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac48633.2019.8997201 |
| Popis: | A robust fifth-degree desensitized cubature Kalman filter (5thDCKF) is proposed to estimates nonlinear state with uncertain parameters. State error sensitivities of the states and covariance about uncertain parameters are propagated by using the fifth-degree cubature points with weights. Then, a novel cost-function is modified by penalizing cost-function, which only includes trace of the posteriori covariance, with state error sensitivities and their weights. A closed-form gain matrix is calculated by minimizing the novel designed cost-function, and this 5thDCKF algorithm is derived. Numerical simulation with a falling body model demonstrates the good performance of the presented 5thDCKF. |
| Databáze: | OpenAIRE |
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