Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment
Two-Dimensional Monte Carlo Filter for a Non-Gaussian Environment
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DOI：
10.3390/electronics10121385
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发表时间：
2021-06
期刊：
影响因子：
2.9
通讯作者：
Xingzi Qiang;R. Xue;Yanbo Zhu
中科院分区：
文献类型：
--
作者：
Xingzi Qiang;R. Xue;Yanbo Zhu
在非高斯环境中，卡尔曼滤波器的精度可能会降低。本文提出了一种二维蒙特卡罗滤波器来克服非高斯环境下滤波的挑战。首次提出了二维蒙特卡罗（TMC）方法，以提高抽样的效率。在此基础上，提出了TMC滤波器（TMCF）算法来解决非高斯滤波问题。在TMCF中，粒子均匀地部署在采样间隔的置信区间内，并基于贝叶斯推理计算它们的权重。然后，后验分布更准确地描述与更少的粒子和它们的权重。与PF不同，TMCF通过一系列权值的计算完成分布的传递，并在置信区间内使用粒子占据状态空间。数值仿真表明，在二维线性/高斯环境中，TMCF的精度接近卡尔曼滤波（KF）（误差约为10 - 6）。在二维线性/非高斯系统中，与粒子滤波相比，TMCF的精度提高了0.01，计算时间从0.20 s减少到0.067 s。
In a non-Gaussian environment, the accuracy of a Kalman filter might be reduced. In this paper, a two- dimensional Monte Carlo Filter is proposed to overcome the challenge of the non-Gaussian environment for filtering. The two-dimensional Monte Carlo (TMC) method is first proposed to improve the efficacy of the sampling. Then, the TMC filter (TMCF) algorithm is proposed to solve the non-Gaussian filter problem based on the TMC. In the TMCF, particles are deployed in the confidence interval uniformly in terms of the sampling interval, and their weights are calculated based on Bayesian inference. Then, the posterior distribution is described more accurately with less particles and their weights. Different from the PF, the TMCF completes the transfer of the distribution using a series of calculations of weights and uses particles to occupy the state space in the confidence interval. Numerical simulations demonstrated that, the accuracy of the TMCF approximates the Kalman filter (KF) (the error is about 10−6) in a two-dimensional linear/ Gaussian environment. In a two-dimensional linear/non-Gaussian system, the accuracy of the TMCF is improved by 0.01, and the computation time reduced to 0.067 s from 0.20 s, compared with the particle filter.