TY - GEN
T1 - An efficient multiple particle filter based on the variational Bayesian approach
AU - Ait-El-Fquih, Boujemaa
AU - Hoteit, Ibrahim
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2016/2/1
Y1 - 2016/2/1
N2 - This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.
AB - This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.
UR - http://hdl.handle.net/10754/595410
UR - http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7394338
UR - http://www.scopus.com/inward/record.url?scp=84963788734&partnerID=8YFLogxK
U2 - 10.1109/ISSPIT.2015.7394338
DO - 10.1109/ISSPIT.2015.7394338
M3 - Conference contribution
SN - 9781509004812
SP - 252
EP - 257
BT - 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -