A general approach for designing the MWGS-based information-form Kalman filtering methods
| Autor: | Julia V. Tsyganova, Andrey V. Tsyganov, Maria V. Kulikova |
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| Rok vydání: | 2020 |
| Předmět: |
Statement (computer science)
0209 industrial biotechnology Correctness Computer science General Engineering 02 engineering and technology Kalman filter 020901 industrial engineering & automation Filter (video) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Orthogonalization Algorithm Information filtering system |
| Zdroj: | European Journal of Control. 56:86-97 |
| ISSN: | 0947-3580 |
| Popis: | The paper addresses a general approach to MWGS (Modified Weighted Gram-Schmidt) orthogonalization based Kalman filtering (KF) implementation methods. We propose two new numerically favored and convenient array information formulations of the MWGS-based KF that are the MWGS-based array Information Filter (algorithm MWGS-aIF) and the extended MWGS-based array Information Filter (algorithm eMWGS-aIF). To confirm the correctness of our results, we have proved that the newly constructed MWGS-based array computational schemes are algebraically equivalent to the “straight” (conventional) information filter. Although all these information-type algorithms are theoretically equivalent, their computational properties are different. The newly proposed algorithms are numerically robust to machine roundoff errors due to the numerically stable orthogonal transformations applied on each iteration. The obtained numerical results confirm this statement. Additionally, algorithm eMWGS-aIF has the extended array form, i. e., it allows for updating all required filter quantities with the use of the numerically stable MWGS orthogonalization procedure, only. Thus, our results extend the existing class of numerically efficient KF implementation methods and can be used in practical applications. |
| Databáze: | OpenAIRE |
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