The Adaptive Kalman Filter in Aircraft Control and Navigation Systems
Alexander A. Afonin, Denis A. Mikhaylin, Andrey S. Sulakov, Alexey P. Moskalev · 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · 2020
This paper describes our approach to tackle the task of achieving optimal estimation while the ongoing changes in accuracy characteristics of measuring instruments are not known in advance. Imprecise data on characteristics of measurement uncertainties can significantly impair accuracy of the state vector's elements estimation. Since accuracy of the measuring complex may vary during a dynamic system operation, the a priori data on measuring instruments' statistical characteristics do not always correspond to actual current values. In this connection, it is reasonable to supplement the Kalman filtering algorithm by a procedure enabling to make these characteristics more precise. Such procedure determines characteristics of the updatable sequence aiming to improve accuracy of statistical characteristics of the measurement uncertainties and consider in the Riccati matrix differential equation. This paper also provides the modelling results confirming effectiveness of the proposed approach.