Hybrid Particle Filtering Based on an Elitist Resampling Scheme
Hybrid Particle Filtering Based on an Elitist Resampling Scheme
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DOI：
10.1109/sam.2018.8448400
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发表时间：
2018-07
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影响因子：
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通讯作者：
Mhd Modar Halimeh;Christian Huemmer;Andreas Brendel;Walter Kellermann
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文献类型：
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作者：
Mhd Modar Halimeh;Christian Huemmer;Andreas Brendel;Walter Kellermann
介绍了精英重采样粒子滤波算法(ERPF)。ERPF是一种基于粒子的进化选择来组合通用粒子过滤器(即SIS和SIR粒子过滤器)的方法，它为所选的粒子组引入了长期记忆。因此，ERPF旨在融合例如稳健性和计算效率的优点，并减轻每个滤波器的缺点，例如退化和样本贫乏。本文提出了两种形式的ERPF：离散ERPF(DERPF)和连续ERPF(CERPF)，据作者所知，它们代表了粒子滤波的新形式。使用两个成熟的基准模型将所提出的两种混合算法与相应的原始滤波器进行比较，以说明进化组合提供了更好的估计精度，并且对异常值表现出更强的稳健性。
In this paper, the elitist resampling particle filter (ERPF) is introduced. The ERPF is an approach to combine generic particle filters, i.e., the SIS and SIR particle filter, based on an evolutionary selection of particles, which introduces a longterm memory to the selected group of the particles. Thereby, the ERPF aims at fusing the advantages, e.g., robustness and computational efficiency, and mitigating the drawbacks of each filter, e.g., degeneracy and sample impoverishment. Two variants of the ERPF are presented in this paper: the discrete ERPF (DERPF) and the continuous ERPF (CERPF), which to the authors' knowledge represent new forms of particle filters. The two proposed hybrids are compared to the corresponding original filters using two well-established benchmark models to illustrate that the evolutionary combinations provide a better estimation accuracy and exhibit an increased robustness against outliers.