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A filter algorithm for multi-measurement nonlinear system with parameter perturbation

机译:具有参数摄动的多尺度非线性系统的滤波算法

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摘要

An improved interacting multiple models particle filter (IMM-PF) algorithm is proposed for multi-measurement nonlinear system with parameter perturbation. It divides the perturbation region into sub-regions and assigns each of them a particle filter. Hence the perturbation problem is converted into a multi-model filters problem. It combines the multiple measurements into a fusion value according to their likelihood function. In the simulation study, we compared it with the IMM-KF and the H-infinite filter; the results testify to its advantage over the other two methods.
机译:针对带有参数摄动的多尺度非线性系统,提出了一种改进的交互多模型粒子滤波算法。它将扰动区域划分为子区域,并为每个子区域分配一个粒子过滤器。因此,摄动问题转化为多模型滤波器问题。根据它们的似然函数,它将多个测量值合并为一个融合值。在仿真研究中,我们将其与IMM-KF和H无限滤波器进行了比较;结果证明了它相对于其他两种方法的优势。

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