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Linear Generalizeg Kalman-Bucy Filter. / Tovstik, T. M. ; Tovstik, P.E.
в: Vestnik St. Petersburg University: Mathematics, Том 52, № 4, 2019, стр. 401-408.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Linear Generalizeg Kalman-Bucy Filter
AU - Tovstik, T. M.
AU - Tovstik, P.E.
N1 - Tovstik, T.M. & Tovstik, P.E. Vestnik St.Petersb. Univ.Math. (2019) 52: 401. https://doi.org/10.1134/S1063454119040113
PY - 2019
Y1 - 2019
N2 - The linear generalized Kalman–Bucy filter problem has been studied in this work. A sum of a useful signal and a noise is an observed process. A signal and a noise are independent stationary auto-regressive processes with orders exceeding one. The filter estimates a signal, using an observed process. Two algorithms of filter are considered: a recurrent one and a direct one. Within the recurrent one, to find the next estimate of a signal, we use the current observation and several previous filter estimates. The direct algorithm uses all previous observations directly. The errors of estimates are found for both algorithms. The advantages and disadvantages of both algorithms are discussed in this paper. Calculations at the recurrent algorithm depend on the observation time. The direct algorithm is reduced to a linear algebraic system such that its order increases in time. On the other hand, the direct algorithm always converges in time, while this is not guaranteed for the recurrent algorithm. Numerical examples are given here.
AB - The linear generalized Kalman–Bucy filter problem has been studied in this work. A sum of a useful signal and a noise is an observed process. A signal and a noise are independent stationary auto-regressive processes with orders exceeding one. The filter estimates a signal, using an observed process. Two algorithms of filter are considered: a recurrent one and a direct one. Within the recurrent one, to find the next estimate of a signal, we use the current observation and several previous filter estimates. The direct algorithm uses all previous observations directly. The errors of estimates are found for both algorithms. The advantages and disadvantages of both algorithms are discussed in this paper. Calculations at the recurrent algorithm depend on the observation time. The direct algorithm is reduced to a linear algebraic system such that its order increases in time. On the other hand, the direct algorithm always converges in time, while this is not guaranteed for the recurrent algorithm. Numerical examples are given here.
KW - Kalman–Bucy filter
KW - recurrent and direct algorithms
KW - high-order auto-regressive processes
UR - https://link.springer.com/article/10.1134/S1063454119040113
M3 - Article
VL - 52
SP - 401
EP - 408
JO - Vestnik St. Petersburg University: Mathematics
JF - Vestnik St. Petersburg University: Mathematics
SN - 1063-4541
IS - 4
ER -
ID: 49338437