TY  - GEN
CY  - Florence, Italy
TI  - Multi-modal filtering for non-linear estimation
Y1  - 2014/07/14/
SN  - 1520-6149
AV  - public
N2  - Multi-modal densities appear frequently in time series and practical applications. However, they are not well represented by common state estimators, such as the Extended Kalman Filter and the Unscented Kalman Filter, which additionally suffer from the fact that uncertainty is often not captured sufficiently well. This can result in incoherent and divergent tracking performance. In this paper, we address these issues by devising a non-linear filtering algorithm where densities are represented by Gaussian mixture models, whose parameters are estimated in closed form. The resulting method exhibits a superior performance on nonlinear benchmarks.
UR  - https://doi.org/10.1109/ICASSP.2014.6855154
ID  - discovery10083728
EP  - 7983
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SP  - 7979
KW  - Science & Technology
KW  -  Technology
KW  -  Acoustics
KW  -  Engineering
KW  -  Electrical & Electronic
KW  -  Engineering
KW  -  State estimation
KW  -  Non-linear dynamical systems
KW  -  Non-Gaussian filtering
KW  -  Gaussian sum
PB  - IEEE
A1  - Kamthe, S
A1  - Peters, J
A1  - Deisenroth, MP
ER  -