Open AccessTracking of pendulum by particle smoother
Author(s) -
Mohan RamVemuri,
Hima BinduBade,
S. Koteswara Rao,
V Gopi Tilak
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.7.10281
Subject(s) - smoothing , particle filter , tracking (education) , computation , algorithm , computer science , monte carlo method , kalman filter , particle (ecology) , monte carlo localization , auxiliary particle filter , series (stratigraphy) , mathematical optimization , mathematics , ensemble kalman filter , extended kalman filter , artificial intelligence , computer vision , statistics , psychology , pedagogy , oceanography , geology , paleontology , biology
The series of tracking algorithms accelerated from linear state to non-linear state estimations like the Particle filter.Due to its vibrant computation,tracking signal gets divergedat peaks.Smoothing makes perfect estimation possible, even at that minute portions by modifying its trace based on all the prior measurement values.So, a Particle smoother is used which uses Monte Carlo approximations for smoothing in a non-linear system. Different types of Particle Smoothers can be implemented by using various algorithms. Here, a Backward Simulation Particle smoother is used which is relatively less degenerate than other smoothing algorithms.