A kind of fast Gaussian particle filter based on Artificial Fish School Algorithm

Abstract

This paper proposes an improved Gaussian particle filter integratingthe Artificial Fish School Algorithm to optimise the measured values to improve the overall estimation accuracy of the system. Meanwhile, it also solves the problems of susceptibility to interference and insufficient estimation accuracy in nonlinear systems. Furthermore, since the calculation time of the fusion algorithm increases, in order to ensure the speed of state estimation, the linear transformation of standard particle swarm is used to replace the particle sampling link of Gaussian particle filter. Simulation results show that the calculation speed of a fast Gaussian Particle Filter based on the Artificial Fish School Algorithm is 21.7% faster than the Particle Filter based on the Artificial Fish School Algorithm. Compared with Particle Filter, Gaussian particle filter, and the Artificial Fish School Algorithm, the proposed algorithm has a higher accuracy.

KEYWORDS:

• Artificial fish school
• Gaussian particle filtering
• linear transformation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Aeronautical Science Foundation of China [grant numbers 2018ZC52037, 2017ZC52017]; and National Natural Science Foundation of China [grant number 51505221].

Notes on contributors

Zhaihe Zhou

Zhou Zhaihe (1974-), male, PhD, currently an associate professor in the School of Automation, Nanjing University of Aeronautics and Astronautics. His main research direction is electromechanical control and automation, data fusion and measurement and control systems, [email protected].

Jingmin Ma

Ma Jingmin (1995-), female, master, now a graduate student in the School of Automation, Nanjing University of Aeronautics and Astronautics, the main research direction is data fusion, [email protected].

Qiqi Liu

Liu Qiqi (1995-), female, master, currently a graduate student of the School of Automation, Nanjing University of Aeronautics and Astronautics, the main research method is data [email protected].

Qingxi Zeng

Zeng Qingxi, male, PhD, currently an associate professor in the School of Automation, Nanjing University of Aeronautics and Astronautics. His main research direction is robot navigation, environment perception and control technology, [email protected].

Xiangrui Tian

Tian Xiangrui, male, PhD, currently an intermediate researcher at Nanjing University of Aeronautics and Astronautics, research direction is intelligent perception of robots, collaborative technology of multiple unmanned systems, [email protected].