Dynamic state estimation using particle filter and adaptive vector quantizer
Dynamic state estimation using particle filter and adaptive vector quantizer
复制标题
DOI：
10.1109/cira.2009.5423166
复制
发表时间：
2009-12
期刊：
影响因子：
--
通讯作者：
T. Nishida;Wataru Kogushi;N. Takagi;S. Kurogi
中科院分区：
文献类型：
--
作者：
T. Nishida;Wataru Kogushi;N. Takagi;S. Kurogi
粒子滤波（PF）是一种利用大量粒子对动态非高斯概率分布进行离散逼近的方法，其运算速度快，适合在线应用。然而，在传统方法中，使用粒子的加权平均值或最大加权值作为滤波输出，而忽略了大多数粒子的信息。另一方面，提出了一种不依赖于初始条件而实现高速自适应的矢量量化（AVQ）算法——竞争重新初始化学习（CRL）。然后，在本研究中，提出了一种结合PF和CRL提取概率密度分布形状信息的方法。仿真结果表明，该方法具有较快的自适应性能和鲁棒性。
Particle filter (PF) is a method for discrete approximation of dynamic and non-Gaussian probability distribution by using numerous particles, and its procedure can execute at high speed and is suitable for on-line applications. However, in conventional methods, a weighted average value or a maximum weighted value of particles is used as a filter output, and information on most particles is disregarded. On the other hand, an adaptive vector quantization (AVQ) algorithm called competitive reinitialization learning (CRL) that can achieve high-speed adaptation without depending on initial conditions has been proposed. Then, in this research, a method for extracting information on shape of probability density distributions by combining PF with CRL is proposed. Moreover, a rapid adaptation performance and the robustness of the proposed method are shown by the simulations.