TY - GEN
T1 - A Probabilistic Approach for Adaptive State-Space Partitioning
AU - Vila-Valls, Jordi
AU - Closas, Pau
AU - Bugallo, Monica F.
AU - Miguez, Joaquin
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/29
Y1 - 2018/8/29
N2 - The multiple Bayesian filtering approach is based on the partitioning of the state-space in several lower dimensional subspaces, combined with a set of parallel filters that characterize the marginal subspace posteriors. This solution has been shown to perform well and solve some of the problems typically suffered by standard Bayesian filters, such as the curse-of-dimensionality, in some scenarios. An inherent problem in the application of multiple Gaussian filters (MGF) and multiple particle filters (MPF) proposed in the literature is how to partition the state-space. A closed answer does not exist because this is an application-dependent problem. In this contribution we further elaborate on the multiple filtering approach, and propose a probabilistic adaptive state-partitioning strategy based on the crosscorrelation computed at each filter.
AB - The multiple Bayesian filtering approach is based on the partitioning of the state-space in several lower dimensional subspaces, combined with a set of parallel filters that characterize the marginal subspace posteriors. This solution has been shown to perform well and solve some of the problems typically suffered by standard Bayesian filters, such as the curse-of-dimensionality, in some scenarios. An inherent problem in the application of multiple Gaussian filters (MGF) and multiple particle filters (MPF) proposed in the literature is how to partition the state-space. A closed answer does not exist because this is an application-dependent problem. In this contribution we further elaborate on the multiple filtering approach, and propose a probabilistic adaptive state-partitioning strategy based on the crosscorrelation computed at each filter.
KW - Adaptive state partitioning
KW - correlated subspaces
KW - multiple Gaussian filtering
KW - uncertainty exchange
UR - https://www.scopus.com/pages/publications/85053838352
U2 - 10.1109/SSP.2018.8450821
DO - 10.1109/SSP.2018.8450821
M3 - Conference contribution
AN - SCOPUS:85053838352
SN - 9781538615706
T3 - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
SP - 363
EP - 367
BT - 2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Statistical Signal Processing Workshop, SSP 2018
Y2 - 10 June 2018 through 13 June 2018
ER -