Local-loop Particle Filter Based on Artificial Fish Algorithm of Big Data

DOI

10.2991/msam-18.2018.44How to use a DOI?

Keywords

big data; filtering algorithm; particle filtering; extended Kalman filter; artificial fish algorithm

Abstract

In the paper, we proposed big data novel filtering method – Local-loop Particle Filter Based on the Artificial Fish Algorithm (LPF-AF) for nonlinear dynamic systems. Particle filtering algorithm has been widely used in solving nonlinear/non Gaussian filtering problems. The proposal distribution is the key issue of the particle filtering, which will greatly influence the performance of algorithm. In the proposed LPF-AF, the local searching of AF is used to regenerate sample particles, which can make the proposal distribution more closed to the poster distribution. There are mainly two steps in the proposed filter. In the first step of LPF-AF, extended kalman filter was used as proposal distribution to generate particles, then means and variances of the proposal distribution can be calculated. In the second step, some particles move to toward the particle with the biggest weights. The proposed LPF-AF algorithm was compared with other several filtering algorithms and the experimental results show that means and variances of LPF-AF are lower than other filtering algorithms.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian Yu
AU  - Yu Yan
PY  - 2018/07
DA  - 2018/07
TI  - Local-loop Particle Filter Based on Artificial Fish Algorithm of Big Data
BT  - Proceedings of the 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018)
PB  - Atlantis Press
SP  - 208
EP  - 211
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-18.2018.44
DO  - 10.2991/msam-18.2018.44
ID  - Yu2018/07
ER  -