Abstract
The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved.
Highlights
The particle filter (PF)[1] is a filtering technology based on the Monte Carlo simulation principle to realize recursive Bayesian estimation
The particle filtering algorithm[1] is a Bayesian inference process based on the sequential Monte Carlo method, which is widely used in robot positioning and tracking.[24]
In order to ensure the effectiveness of the particle filter, this paper introduces the particle filter positioning error threshold, which is the Euclidean distance between the particle filter positioning results and the geometric center
Summary
The particle filter (PF)[1] is a filtering technology based on the Monte Carlo simulation principle to realize recursive Bayesian estimation. Its characteristic is that the system has no linear model and Gaussian noise requirements, it can solve the nonlinear non-Gaussian filtering problem,[2] and the calculation results can approach the optimal estimation with an increase in the number of particles. With the rapid improvement in hardware computing power, this method has been applied in the fields of target tracking,[3] location,[4,5,6] aviation/spacecraft attitude estimation,[7,8] fault detection, and isolation.[9,10]
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