INFLUENCE OF A PRIORI INFORMATION ON THE MULTIPLE MODEL ALGORITHM CONVERGENCE DURING TRACKING A BALLISTIC TARGET

2020 ◽  
Author(s):  
Д.С. Голенко ◽  
М.И. Сычев
Keyword(s):  
Mathematical Modeling ◽  
Kalman Filters ◽  
A Priori ◽  
Multiple Model ◽  
A Priori Information ◽  
Extended Kalman Filters ◽  
Sigma Point ◽  
Passive Antenna ◽  
Priori Information ◽  
Model Algorithm

Рассмотрена задача сопровождения маневрирующего баллистического объекта на этапе входа в атмосферу с помощью пассивной антенной решетки. Предложено использовать многомодельный алгоритм на основе расширенного и сигма-точечного фильтров Калмана. Проанализировано влияние точности априорной информации на сходимость многомодельного алгоритма. С помощью математического моделирования проведено сравнение с одиночными фильтрами Калмана. The problem of reentry ballistic target tracking with a passive antenna array is considered. Multiple model algorithm based on extended and sigma-point Kalman filters is proposed. A priori information accuracy influence on the convergence of multiple model algorithm is analyzed. Using mathematical modeling, the results were compared with regular extended Kalman filters.

scholarly journals Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

2004 ◽  
Vol 128 (2) ◽  
pp. 281-287 ◽  
Author(s):  
P. Dewallef ◽  
C. Romessis ◽  
O. Léonard ◽  
K. Mathioudakis
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Diagnostic Method ◽  
A Priori ◽  
Aircraft Engine ◽  
Bayesian Belief Network ◽  
A Priori Information ◽  
Time Step ◽  
Classification Techniques ◽  
Priori Information

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated, and its advantages over individual constituent methods are presented.

scholarly journals Combining Classification Techniques With Kalman Filters for Aircraft Engine Diagnostics

2004 ◽  
Author(s):  
P. Dewallef ◽  
C. Romessis ◽  
O. Le´onard ◽  
K. Mathioudakis
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Diagnostic Method ◽  
A Priori ◽  
Aircraft Engine ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
A Priori Information ◽  
Time Step ◽  
Priori Information

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Networks (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm has improved identification capability in comparison to the stand alone Kalman filter. The paper focuses on the way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated and its advantages over individual constituent methods are shown.

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