Improving Particle Filter Performance by Smoothing Observations
Improving Particle Filter Performance by Smoothing Observations
复制标题
通过平滑观测来提高粒子过滤器性能
DOI：
10.1175/mwr-d-17-0349.1
复制
发表时间：
2017
影响因子：
3.2
通讯作者：
W. Kleiber
中科院分区：
文献类型：
--
作者：
Gregor A. Robinson;I. Grooms;W. Kleiber
这篇文章表明，在小尺度上增加观测方差可以减少所需的合奏大小，以避免崩溃的粒子滤波的空间扩展动力学，并提高在大尺度上的不确定性量化。粒子滤波器的权重取决于集合成员与观测值的一致程度，当少数集合成员获得大部分权重时，就会发生崩溃。崩溃导致灾难性的方差低估。增加观测误差模型中的小尺度方差，通过不强调集合成员和观测之间的小尺度差异来降低崩溃的发生率。这样做会平滑后验均值，但不会平滑单个集合成员。两个选项，用于实施拟议的观测误差模型进行了说明。以离散化椭圆微分算子作为观测误差协方差矩阵提供了在小尺度方法中增长的谱的期望性质。这种选择还引入了可扩展计算技术，包括多重网格求解器和相应的积分算子的多分辨率近似的结构。或者，观测值可以被平滑，然后在独立误差的假设下被同化，这相当于在小尺度上假设大误差。线性随机偏微分方程，它显着减少粒子滤波崩溃的发生，同时保持精度的方法证明。它还将连续排名概率得分提高了高达25%，这表明加权集合更准确地代表了真实分布。该方法是兼容的其他技术，以提高粒子滤波器的性能。
This article shows that increasing the observation variance at small scales can reduce the ensemble size required to avoid collapse in particle filtering of spatially extended dynamics and improve the resulting uncertainty quantification at large scales. Particle filter weights depend on how well ensemble members agree with observations, and collapse occurs when a few ensemble members receive most of the weight. Collapse causes catastrophic variance underestimation. Increasing small-scale variance in the observation error model reduces the incidence of collapse by de-emphasizing small-scale differences between the ensemble members and the observations. Doing so smooths the posterior mean, though it does not smooth the individual ensemble members. Two options for implementing the proposed observation error model are described. Taking a discretized elliptic differential operator as an observation error covariance matrix provides the desired property of a spectrum that grows in the approach to small scales. This choice also introduces structure exploitable by scalable computation techniques, including multigrid solvers and multiresolution approximations to the corresponding integral operator. Alternatively the observations can be smoothed and then assimilated under the assumption of independent errors, which is equivalent to assuming large errors at small scales. The method is demonstrated on a linear stochastic partial differential equation, where it significantly reduces the occurrence of particle filter collapse while maintaining accuracy. It also improves continuous ranked probability scores by as much as 25%, indicating that the weighted ensemble more accurately represents the true distribution. The method is compatible with other techniques for improving the performance of particle filters.
影响因子：
5.7
作者：
Agapiou, S.;Papaspiliopoulos, O.;Stuart, A. M.
通讯作者：
Stuart, A. M.
DOI：
10.1080/16000870.2017.1283809
发表时间：
2017-01-01
影响因子：
2
作者：
Morzfeld, Matthias;Hodyss, Daniel;Snyder, Chris
通讯作者：
Snyder, Chris