论文标题
通过非线性kalman滤波器库进行隐式粒子过滤
Implicit Particle Filtering via a Bank of Nonlinear Kalman Filters
论文作者
论文摘要
隐式粒子过滤器试图通过识别目标分布高概率区域中的粒子来减轻粒子退化。这项研究是出于需要增强实施这种方法的计算障碍的动力。我们研究了隐式粒子滤波器与卡尔曼滤波器的连接，然后根据非线性卡尔曼滤波器的库对隐式粒子过滤器进行新颖的实现。这种认识在计算上更加适合和有效。
The implicit particle filter seeks to mitigate particle degeneracy by identifying particles in the target distribution's high-probability regions. This study is motivated by the need to enhance computational tractability in implementing this approach. We investigate the connection of the particle update step in the implicit particle filter with that of the Kalman filter and then formulate a novel realization of the implicit particle filter based on a bank of nonlinear Kalman filters. This realization is more amenable and efficient computationally.
