TY - GEN
T1 - Second Order Subspace Statistics for Adaptive State-Space Partitioning in Multiple Particle Filtering
AU - Perez-Vieites, S.
AU - Vila-Valls, J.
AU - Bugallo, M. F.
AU - Miguez, J.
AU - Closas, P.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - One of the main challenges in nonlinear Bayesian filtering is the so-called curse of dimensionality, that is, the computational complexity increase and associated performance degradation in high-dimensional systems. In the context of particle filtering (PF), a possible solution to mitigate such performance loss is the multiple PF (MPF) approach, where the original state is partitioned into several lower dimensional subspaces, and a set of interconnected PFs are used to characterize the marginal subspace posteriors. Two key issues are: i) how to partition the state, which is application dependent, and ii) how to let the filters (i.e., subspaces) fuse or merge depending on the time-varying conditions of the system, in order to improve the overall estimation performance. We propose a probabilistic approach to the adaptive state-partitioning problem within the MPF, which is based on the computation of subspace second order statistics. An illustrative multiple target tracking example is considered to support the discussion.
AB - One of the main challenges in nonlinear Bayesian filtering is the so-called curse of dimensionality, that is, the computational complexity increase and associated performance degradation in high-dimensional systems. In the context of particle filtering (PF), a possible solution to mitigate such performance loss is the multiple PF (MPF) approach, where the original state is partitioned into several lower dimensional subspaces, and a set of interconnected PFs are used to characterize the marginal subspace posteriors. Two key issues are: i) how to partition the state, which is application dependent, and ii) how to let the filters (i.e., subspaces) fuse or merge depending on the time-varying conditions of the system, in order to improve the overall estimation performance. We propose a probabilistic approach to the adaptive state-partitioning problem within the MPF, which is based on the computation of subspace second order statistics. An illustrative multiple target tracking example is considered to support the discussion.
KW - Adaptive state-space partitioning
KW - multiple particle filtering
KW - second order subspace statistics
UR - https://www.scopus.com/pages/publications/85082389800
U2 - 10.1109/CAMSAP45676.2019.9022449
DO - 10.1109/CAMSAP45676.2019.9022449
M3 - Conference contribution
AN - SCOPUS:85082389800
T3 - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
SP - 609
EP - 613
BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Y2 - 15 December 2019 through 18 December 2019
ER -