集成卡尔曼滤波器与粒子滤波器的融合
Bridging the ensemble Kalman and particle filter
摘要：
在蒙特卡罗非线性滤波的许多应用中，传播步骤在计算上是昂贵的，因此，样本量是有限的。对于小的样本量，更新步骤变得至关重要。粒子滤波有一个众所周知的问题，即样本退化。包围卡尔曼滤波避免了这一点，但代价是错误地处理预测分布的非高斯特征。在这里，我们介绍了一个程序，使连续过渡的伽玛索引[0，1]之间的合奏和粒子滤波器更新。我们建议自动选择的参数伽玛，使更新保持尽可能接近粒子滤波器更新，以避免退化。在各种例子中，我们表明，这一过程导致更新，即使在高维的情况下，也能够处理预测样本的非高斯特征。
英文摘要：
In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the update step becomes crucial. Particle filtering suffers from the well-known problem of sample degeneracy. Ensemble Kalman filtering avoids this, at the expense of treating non-Gaussian features of the forecast distribution incorrectly. Here we introduce a procedure which makes a continuous transition indexed by gamma in [0,1] between the ensemble and the particle filter update. We propose automatic choices of the parameter gamma such that the update stays as close as possible to the particle filter update subject to avoiding degeneracy. In various examples, we show that this procedure leads to updates which are able to handle non-Gaussian features of the prediction sample even in high-dimensional situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
