An Application of the Kalman Filter Recursive Algorithm to Estimate the Gaussian Errors by Minimizing the Symmetric Loss Function

Kalman filtering is a linear quadratic estimation (LQE) algorithm that uses a time series of observed data to produce estimations of unknown variables. The Kalman filter (KF) concept is widely used in applied mathematics and signal processing. In this study, we developed a methodology for estimating...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-02, Vol.13 (2), p.240
Main Authors: Busu, Cristian, Busu, Mihail
Format: Article
Language:English
Subjects:
Algorithms
Applications of mathematics
contraction property
extended Kalman filter (EKF)
Gaussian errors
Kalman filters
linear quadratic estimation (LQE)
Noise
Normal distribution
Random variables
recursive algorithm
Signal processing
Statistical analysis
unscented Kalman filter (UKF)
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Summary:Kalman filtering is a linear quadratic estimation (LQE) algorithm that uses a time series of observed data to produce estimations of unknown variables. The Kalman filter (KF) concept is widely used in applied mathematics and signal processing. In this study, we developed a methodology for estimating Gaussian errors by minimizing the symmetric loss function. Relevant applications of the kinetic models are described at the end of the manuscript.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13020240