标题： 正则化粒子滤波器的重采样策略的批判性分析 显示英文标题

标题： A critical analysis of resampling strategies for the regularized particle filter

摘要： 我们分析了不同重采样策略在正则化粒子滤波器参数估计中的性能。特别是，基于在线性高斯情况下获得的解析洞见，我们展示了系统地重采样可以防止滤波密度收敛到真实的后验分布。我们讨论了几种克服这一限制的方法，包括核带宽调节，并提供了证据表明由此产生的粒子滤波器明显优于传统的自助粒子滤波器。我们的结果得到了线性教科书例子、逻辑映射和非线性植物生长模型上的数值模拟的支持。 显示英文摘要

摘要： We analyze the performance of different resampling strategies for the regularized particle filter regarding parameter estimation. We show in particular, building on analytical insight obtained in the linear Gaussian case, that resampling systematically can prevent the filtered density from converging towards the true posterior distribution. We discuss several means to overcome this limitation, including kernel bandwidth modulation, and provide evidence that the resulting particle filter clearly outperforms traditional bootstrap particle filters. Our results are supported by numerical simulations on a linear textbook example, the logistic map and a non-linear plant growth model.