Gaussian Mixture Unscented Particle Filter with Adaptive Residual Resample for Nonlinear Model

DOI

10.2991/icicci-15.2015.2How to use a DOI?

Keywords

Keywords-Target tracking; Gaussian mixture; Unscented particle filter; Residual resample

Abstract

Abstract—To solve nonlinear non-Gaussian filter problems in target tracking, Gaussian mixture unscented particle filter with adaptive residual resample algorithm is proposed. Gaussian mixture unscented particle filter is utilized as importance density to improve the estimation accuracy evidently. By introducing adaptive residual resample, the new algorithm overcomes the defects of general resample algorithm. To evaluate the proposed algorithm, the random surfer dynamic model and range-rate measurement are involved as nonlinear models with two static sensors. Simulation results show that the proposed algorithm performs robust and effective. As a consequence, compared with the general Gaussian particle filter, the proposed algorithm is more accurate in estimated state and more diverse in particles.

Copyright

© 2015, 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  - Na Zhang
AU  - Xinxin Yang
PY  - 2015/09
DA  - 2015/09
TI  - Gaussian Mixture Unscented Particle Filter with Adaptive Residual Resample for Nonlinear Model
BT  - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics
PB  - Atlantis Press
SP  - 5
EP  - 10
SN  - 1951-6851
UR  - https://doi.org/10.2991/icicci-15.2015.2
DO  - 10.2991/icicci-15.2015.2
ID  - Zhang2015/09
ER  -