Abstract
Abstract Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion. The gravitation field algorithm simulates the solar nebular disk model, and introduces the virtual central attractive force and virtual rotation repulsion force between particles. The particles are moves rapidly to the high-likelihood region under action of the virtual central attractive force. The virtual rotation repulsion force makes the particles keep a certain distance from each other. These operations improve estimation performance, avoid overlapping of particles and maintain the diversity of particles. The proposed method is applied into INS/gravity gradient aided navigation, by combining the sea experimental data of an inertial navigation system. Compared with the particle swarm optimization particle filter(PSO-PF) and artificial physics optimized particle filter (APO-PF), the GF-PF has higher position estimate accuracy and faster convergence speed with the same experimental conditions.
Highlights
Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter
This paper proposes a particle filer based on the gravitation field algorithm (GF-PF), and the gravitation field algorithm is introduced into the resampling process to improve particle degradation and sample depletion
Compared with particle swarm optimization particle filter(PSO-PF) [27] and artificial physics optimized particle filter (APO-PF), the simulation results show that the method proposed in this paper can obtain higher positioning accuracy and faster convergence speed under different test conditions
Summary
Abstract: Swarm intelligence method is an effective way to improve the particle degradation and sample depletion of the traditional particle filter. Some scholars applied particle filter algorithm to gravity/gravity gradient aided navigation, and obtain good positioning results. LIU [20, 24] proposes an artificial optimization particle filter algorithm, and applied it to gravity / gravity gradient aided navigation system. It gets a positioning accuracy of 133.6m. Compared with particle swarm optimization particle filter(PSO-PF) [27] and artificial physics optimized particle filter (APO-PF), the simulation results show that the method proposed in this paper can obtain higher positioning accuracy and faster convergence speed under different test conditions. The position is used to revise the positioning error of the INS
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