Monte carlo mixture kalman filter and its application to space-time inversion

2003 ◽  
Vol 36 (16) ◽  
pp. 1263-1268
Author(s):  
Tomoyuki Higuchi ◽  
Jun'ichi Fukuda
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Space Time ◽  
Time Inversion ◽  
Mixture Kalman Filter

scholarly journals A new approach to time-dependent inversion of geodetic data using a Monte Carlo mixture Kalman filter

2004 ◽  
Vol 159 (1) ◽  
pp. 17-39 ◽  
Author(s):  
Jun'ichi Fukuda ◽  
Tomoyuki Higuchi ◽  
Shin'ichi Miyazaki ◽  
Teruyuki Kato
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Geodetic Data ◽  
Time Dependent ◽  
New Approach ◽  
Mixture Kalman Filter

scholarly journals Space-time multilevel Monte Carlo methods and their application to cardiac electrophysiology

2021 ◽  
Vol 433 ◽  
pp. 110164
Author(s):  
S. Ben Bader ◽  
P. Benedusi ◽  
A. Quaglino ◽  
P. Zulian ◽  
R. Krause
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Cardiac Electrophysiology ◽  
Space Time ◽  
Multilevel Monte Carlo

scholarly journals Tracking of pendulum using particle filter with residual resampling

2018 ◽  
Vol 7 (2.7) ◽  
pp. 12
Author(s):  
Penumarty Hiranmayi ◽  
Kola Sai Gowtham ◽  
S Koteswara Rao ◽  
V Gopi Tilak
Keyword(s):  
Monte Carlo ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Horizontal Direction ◽  
Bayesian Filtering ◽  
Discrete State ◽  
Harmonic Motion ◽  
Simple Pendulum ◽  
Importance Resampling

The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consist of discrete state components since under these circumstances the Bayesian approximations do not always work well.

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