Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of stochastic samples that widen the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. The ensemble transport particle filter, and its second order variant, are extended with the proposed rejuvenation approach. Numerical experiments show that modified filters significantly improve the analyses for low dynamical ensemble sizes.
翻译:在粒子过滤器中进行再造是必要的,以防止当粒子数量不足以取样国家空间高概率区域时,当粒子数量不足以取样时,在粒子过滤器中进行重力调整对于防止重量的崩溃是必要的。重整通常以超常方式进行,方法是增加随机样本,扩大共振的支持。这项工作旨在通过引进从气候样本中事先获得的额外信息,改进共振再生方法;在重要取样中使用的动态粒子通过从随机共振缩取的样本加以增强。通过拟议的振兴方法,扩大共振传输粒子过滤器及其第二顺序变异。数字实验表明,经过修改的过滤器大大改进了对低动态共振动大小的分析。