TY - GEN
T1 - Learning by simplified cost-reference particle filtering using biased data
AU - Bugallo, Mónica F.
AU - Lu, Ting
AU - Djuriè, Petar M.
PY - 2007
Y1 - 2007
N2 - In this paper we address the problem of online learning by cost-reference particle filtering combined with Kalman filtering. We propose an efficient learning scheme applicable to problems where some of the unknowns of a dynamic system of interest are linear given the remaining unknowns, which are nonlinear. To that end, we exploit a concept that is analogous to Rao-Blackwellization, and we implement it by using only one Kalman filter. The resulting algorithm is tested and compared to standard particle filtering for the problem of target tracking using bearings-only measurements acquired by two sensors.
AB - In this paper we address the problem of online learning by cost-reference particle filtering combined with Kalman filtering. We propose an efficient learning scheme applicable to problems where some of the unknowns of a dynamic system of interest are linear given the remaining unknowns, which are nonlinear. To that end, we exploit a concept that is analogous to Rao-Blackwellization, and we implement it by using only one Kalman filter. The resulting algorithm is tested and compared to standard particle filtering for the problem of target tracking using bearings-only measurements acquired by two sensors.
UR - https://www.scopus.com/pages/publications/48149093638
U2 - 10.1109/MLSP.2007.4414340
DO - 10.1109/MLSP.2007.4414340
M3 - Conference contribution
AN - SCOPUS:48149093638
SN - 1424415667
SN - 9781424415663
T3 - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
SP - 402
EP - 407
BT - Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
T2 - 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Y2 - 27 August 2007 through 29 August 2007
ER -