TY - JOUR
T1 - Non-linear Gaussian smoothing with Taylor moment expansion
AU - Zhao, Zheng
AU - Särkkä, Simo
N1 - Tallennetaan OA-artikkeli, kun julkaistu
PY - 2022
Y1 - 2022
N2 - This letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.
AB - This letter is concerned with solvingcontinuous-discrete Gaussian smoothing problems by using the Taylor moment expansion (TME) scheme. In the proposed smoothing method, we apply the TME method to approximate the transition density of the stochastic differential equation in the dynamic model. Furthermore, we derive a theoretical error bound (in the mean square sense) of the TME smoothing estimates showing that the smoother is stable under weak assumptions. Numerical experiments show that the proposed smoother outperforms a number of baseline smoothers.
KW - Smoothing methods
KW - Mathematical models
KW - Signal processing algorithms
KW - Numerical models
KW - Approximation algorithms
KW - Stochastic processes
KW - Frequency modulation
UR - http://www.scopus.com/inward/record.url?scp=85124128775&partnerID=8YFLogxK
U2 - 10.1109/LSP.2021.3125831
DO - 10.1109/LSP.2021.3125831
M3 - Article
SN - 1558-2361
VL - 29
SP - 80
EP - 84
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
M1 - 9606583
ER -