Local-loop Particle Filter Based on Artificial Fish Algorithm of Big Data
- 10.2991/msam-18.2018.44How to use a DOI?
- big data; filtering algorithm; particle filtering; extended Kalman filter; artificial fish algorithm
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.
- © 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/).
Cite this article
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 -