The equivalent-weights particle filter in a high-dimensional system
The equivalent-weights particle filter in a high-dimensional system
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DOI：
10.1002/qj.2370
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发表时间：
2015-01-01
影响因子：
8.9
通讯作者：
van Leeuwen, P. J.
中科院分区：
文献类型：
--
作者：
Ades, M.;van Leeuwen, P. J.
一般来说，粒子滤波器需要大量的模型运行，以避免高维系统中的滤波器简并。最近提出的完全非线性等效权重粒子滤波器通过用两种不同的建议过渡密度替换标准模型过渡密度来克服这一要求。第一个建议密度用于将所有粒子松弛到由观察定义的状态空间的高概率区域。然后使用关键的第二个提议密度来确保大多数粒子在观察时具有相同的权重。在这里，我们探讨了该方案在 65 500 维简化海洋模型中的性能。在孪生实验中仅使用 32 个粒子的平均值即可证明等效权重粒子滤波器在匹配真实模型状态方面的成功。特别重要的是，即使观测的数量和空间变异性发生变化，这一点仍然成立。排名直方图的结果不太容易解释，并且可能会受到所使用的参数值的很大影响。本文还探讨了该方案的性能对所选参数值的敏感性，以及与集成模型运行相比，在真实情况下使用不同模型误差参数的效果。
In general, particle filters need large numbers of model runs in order to avoid filter degeneracy in high-dimensional systems. The recently proposed, fully nonlinear equivalent-weights particle filter overcomes this requirement by replacing the standard model transition density with two different proposal transition densities. The first proposal density is used to relax all particles towards the high-probability regions of state space as defined by the observations. The crucial second proposal density is then used to ensure that the majority of particles have equivalent weights at observation time. Here, the performance of the scheme in a high, 65 500 dimensional, simplified ocean model is explored. The success of the equivalent-weights particle filter in matching the true model state is shown using the mean of just 32 particles in twin experiments. It is of particular significance that this remains true even as the number and spatial variability of the observations are changed. The results from rank histograms are less easy to interpret and can be influenced considerably by the parameter values used. This article also explores the sensitivity of the performance of the scheme to the chosen parameter values and the effect of using different model error parameters in the truth compared with the ensemble model runs.