An Improved Robust Adaptive Kalman Filtering Algorithm
| Autor: | Wenxing Fu, Longyun Chi, Liuyang Jiang, Hai Zhang, Zheng Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Navigation system 02 engineering and technology Kalman filter Filter (signal processing) Kalman filtering algorithm Compensation (engineering) 020901 industrial engineering & automation Outlier 0202 electrical engineering electronic engineering information engineering Global Positioning System Measurement uncertainty Errors-in-variables models 020201 artificial intelligence & image processing business Algorithm |
| Zdroj: | 2019 Chinese Control Conference (CCC). |
| DOI: | 10.23919/chicc.2019.8865383 |
| Popis: | Robust adaptive Kalman filter (RAKF) is a method that can not only compensate for the model errors but also resist the measurement outliers. The method reduces the influence of the measurement outliers on the filtering system by reducing the weight of the abnormal observation. After the processing of robust estimation, all the errors are attributed to the inaccuracy of the model. Since the robust estimation is not theoretically quantitative that it can only reduce the observation error to a certain extent, the remaining observation errors are compensated as the model errors, resulting in new estimation errors. To address this problem, a method for detecting the model error is proposed as a trigger condition for RAKF to compensate for the model error, and this constitutes an improved robust adaptive Kalman filter (IRAKF) algorithm. The new algorithm performs the model errors compensation struand then adjust the adaptive factor cter than the original method. The efficacy of the improved algorithm is demonstrated via a traffic running testing with GPS/INS tight integration navigation system. Simulation results indicate that the improved algorithm can effectively reduce the inaccurate adaptive procedure and obtain relatively accurate and stable filtering results. |
| Databáze: | OpenAIRE |
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