TY - GEN
T1 - Particle filtering for high-dimensional systems
AU - Djuric, Petar M.
AU - Bugallo, Monica F.
PY - 2013
Y1 - 2013
N2 - Particle filtering methods aim at tracking probability distributions sequentially in time. One of the main challenges of these methods is their accuracy in high-dimensional state spaces. Namely, it can be shown that if the dimensions of these spaces are sufficiently high, the obtained results by particle filtering are practically useless. In this paper, we propose an approach for addressing this problem. It is based on breaking the high-dimensional distribution of the complete state into smaller dimensional (marginalized) distributions and attempting to track these distributions in a novel way as accurately as possible. We demonstrate the proposed approach with computer simulations.
AB - Particle filtering methods aim at tracking probability distributions sequentially in time. One of the main challenges of these methods is their accuracy in high-dimensional state spaces. Namely, it can be shown that if the dimensions of these spaces are sufficiently high, the obtained results by particle filtering are practically useless. In this paper, we propose an approach for addressing this problem. It is based on breaking the high-dimensional distribution of the complete state into smaller dimensional (marginalized) distributions and attempting to track these distributions in a novel way as accurately as possible. We demonstrate the proposed approach with computer simulations.
UR - https://www.scopus.com/pages/publications/84894192110
U2 - 10.1109/CAMSAP.2013.6714080
DO - 10.1109/CAMSAP.2013.6714080
M3 - Conference contribution
AN - SCOPUS:84894192110
SN - 9781467331463
T3 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
SP - 352
EP - 355
BT - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
T2 - 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Y2 - 15 December 2013 through 18 December 2013
ER -