Open AccessAdaptive iterated particle filter
Author(s) -
Zuo J.Y.,
Jia Y.N.,
Zhang Y.Z.,
Lian W.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2012.4506
Subject(s) - particle filter , ensemble kalman filter , auxiliary particle filter , kernel adaptive filter , adaptive filter , control theory (sociology) , kalman filter , iterated function , extended kalman filter , algorithm , resampling , computer science , filter design , filter (signal processing) , mathematics , artificial intelligence , mathematical analysis , control (management) , computer vision
The adaptive iterated particle filter (AIPF) is presented, where the importance density function is updated iteratively by the particle filter itself when necessary. By using a simulated annealing algorithm with an adaptive annealing parameter, the current measurement can be quickly incorporated into the sampling process, resulting in greatly improved sampling efficiency. Simulation results demonstrate the improved performance of the AIPF over the sampling importance resampling filter, unscented Kalman particle filter and auxiliary particle filter.