论文标题
粒子过滤器精确的daum-huang流动方程的分析解
Analytic solution of the exact Daum-Huang flow equation for particle filters
论文作者
论文摘要
尽管计算能力不断增加，但对非线性系统的状态估计，尤其是在高维度中，这通常是一个棘手的问题。有效的算法通常应用有限维模型来近似状态矢量的概率密度或数值处理估计问题。在2007年，Daum和Huang引入了一种新型的粒子滤波器方法，该方法使用均匀的粒子流进行贝叶斯更新步骤。由于具有不同的性质，因此得出了多种类型的粒子流。这项工作中考虑的确切流是粒子运动的一阶线性普通时间变化不均匀的微分方程。为标量测量情况得出了间隔[0,1]中的一个分析解决方案，该解决方案可以显着更快地计算粒子过滤器的贝叶斯更新步骤。
State estimation for nonlinear systems, especially in high dimensions, is a generally intractable problem, despite the ever-increasing computing power. Efficient algorithms usually apply a finite-dimensional model for approximating the probability density of the state vector or treat the estimation problem numerically. In 2007 Daum and Huang introduced a novel particle filter approach that uses a homotopy-induced particle flow for the Bayesian update step. Multiple types of particle flows were derived since with different properties. The exact flow considered in this work is a first-order linear ordinary time-varying inhomogeneous differential equation for the particle motion. An analytic solution in the interval [0,1] is derived for the scalar measurement case, which enables significantly faster computation of the Bayesian update step for particle filters.
