论文标题
粒子流高斯和粒子过滤器
Particle Flow Gaussian Sum Particle Filter
论文作者
论文摘要
粒子流动粒子流（PFGPF）使用可逆粒子流来产生建议密度。它将预测性和后验分布近似为高斯密度。在本文中，我们使用PFGPF过滤器的库来构建粒子流高斯和粒子滤波器（PFGSPF），该粒子粒子滤镜（PFGSPF）将预测性和后部近似为高斯混合模型。这种近似在复杂的估计问题中很有用，在复杂的估计问题中，单个高斯近似不足。我们将该提出的过滤器与PFGPF和其他人在具有挑战性的数值模拟中进行了比较。
Particle flow Gaussian particle flow (PFGPF) uses an invertible particle flow to generate a proposal density. It approximates the predictive and posterior distributions as Gaussian densities. In this paper, we use bank of PFGPF filters to construct a Particle flow Gaussian sum particle filter (PFGSPF), which approximates the predictive and posterior as Gaussian mixture model. This approximation is useful in complex estimation problems where a single Gaussian approximation is not sufficient. We compare the performance of this proposed filter with PFGPF and others in challenging numerical simulations.
