TY - GEN

T1 - Particle filtering with alpha-stable distributions

AU - Mihaylova, L.

AU - Brasnett, P.

AU - Achim, A.

AU - Bull, D.

AU - Canagarajah, N.

N1 - "©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2005

Y1 - 2005

N2 - In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alpha- stable (SAS) distributions. Sequential Bayesian estimation generally involves recursive estimation of filtering and predictive distributions of unobserved signals from their noisy measurements. In our proposed algorithm, the relevant density functions are approximated by particles drawn from stable distributions. We call this novel technique SAS particle filtering (SASPF). We assess the performance of the SASPF in comparison with the Gaussian Sum particle filter (GSPF) [1] and a standard (non-parametric) particle filter (PF). Results obtained using highly nonlinear models with simulated data show that the SASPF outperforms the GSPF and compares very favorably with the PF.

AB - In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alpha- stable (SAS) distributions. Sequential Bayesian estimation generally involves recursive estimation of filtering and predictive distributions of unobserved signals from their noisy measurements. In our proposed algorithm, the relevant density functions are approximated by particles drawn from stable distributions. We call this novel technique SAS particle filtering (SASPF). We assess the performance of the SASPF in comparison with the Gaussian Sum particle filter (GSPF) [1] and a standard (non-parametric) particle filter (PF). Results obtained using highly nonlinear models with simulated data show that the SASPF outperforms the GSPF and compares very favorably with the PF.

KW - alpha stable distributions

KW - particle filtering

KW - estimation

KW - nonlinear systems DCS-publications-id

KW - inproc-431

KW - DCS-publications-credits

KW - dsp-fa

KW - DCS-publications-personnel-id

KW - 121

U2 - 10.1109/SSP.2005.1628625

DO - 10.1109/SSP.2005.1628625

M3 - Conference contribution/Paper

SN - 0-7803-9403-8

SP - 381

EP - 386

BT - Statistical Signal Processing, 2005 IEEE/SP 13th Workshop on

T2 - IEEE Statistical Signal Processing Workshop

Y2 - 17 July 2005 through 20 July 2005

ER -