一种受生物启发的粒子过滤器改进策略

J. Zhong, Y. Fung
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引用次数: 6

摘要

粒子滤波(PF)是一种基于仿真的模型估计技术。但在粒子更新阶段,经常会出现粒子贫困化和样本量依赖两个问题,这些问题会降低估计结果的准确性。为了避免这些问题,在更新阶段之前将蚁群算法引入到通用粒子滤波中。优化后,粒子样本会向其局部最高后验密度函数靠拢,得到更好的估计结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A biological inspired improvement strategy for Particle Filters
Particle Filters (PF) is a model estimation technique based on simulation. But two problems, namely particle impoverishment and sample size dependency, frequently occur during the particle updating stage and these problems will reduce the accuracy of the estimation results. In order to avoid these problems, Ant Colony Optimization is incorporated into the generic particle filter before the updating stage. After the optimization, particle samples will move closer to their local highest posterior density function and better estimation results can be produced.
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