A new particle filter for high-dimensional state-space models based on intensive and extensive proposal distribution

Viet Phuong Nguyen; Takashi Washio; Tomoyuki Higuchi

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A new particle filter for high-dimensional state-space models based on intensive and extensive proposal distribution

机译：基于密集和广泛提案分配的用于高维状态空间模型的新粒子过滤器

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摘要

This paper proposes novel principles and techniques of a particle filter to estimate dynamic system states under an observed time series data and a state-space model which are possibly non-linear and have the dimensions more than several hundreds. First, we point out two crucial curses of dimensionality and propose three key ideas to overcome them. Second, we propose the novel particle filters implementing these ideas and analyse their mathematical characteristics. Our experimental evaluation demonstrates their significant accuracy, robustness and efficiency for both artificial and real-world problems having large scales.

机译：本文提出了一种粒子滤波器的新原理和新技术，可以在观察到的时间序列数据和状态空间模型下估计动态系统的状态，这些模型可能是非线性的并且具有数百个维度。首先，我们指出了维数的两个至关重要的诅咒，并提出了三个克服它们的关键思想。其次，我们提出了实现这些思想的新型粒子过滤器并分析了它们的数学特征。我们的实验评估表明，它们对于大规模的人工和现实问题均具有显着的准确性，鲁棒性和效率。

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《International journal of knowledge engineering and soft data paradigms》 |2010年第4期|p.284-311|共28页
作者
Viet Phuong Nguyen; Takashi Washio; Tomoyuki Higuchi;
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作者单位

The Institute for Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, Ibaraki-shi, Osaka 567-0047, Japan;

The Institute for Scientific and Industrial Research, Osaka University, 8-1, Mihogaoka, Ibaraki-shi, Osaka 567-0047, Japan;

The Institute of Statistical Mathematics,10-3, Midori-cho, Tachikawa, Tokyo 190-8562, Japan;

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正文语种 eng
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关键词
dynamic systems; state-space model; state estimation; particle filters; high-dimensional state space; curses of dimensionality; approximation of distribution; intensive and extensive proposal distribution; non-parametric smoothings;

机译：动态系统;状态空间模型状态估计颗粒过滤器高维状态空间维度诅咒;分布近似;密集和广泛的提案分发;非参数平滑;

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A new particle filter for high-dimensional state-space models based on intensive and extensive proposal distribution

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