Marginalized particle filters for mixed linear/nonlinear state-space models
Marginalized particle filters for mixed linear/nonlinear state-space models
复制标题
DOI：
10.1109/tsp.2005.849151
复制
发表时间：
2005-07-01
影响因子：
5.4
通讯作者：
Nordlund, PJ
中科院分区：
文献类型：
--
作者：
Schön, T;Gustafsson, F;Nordlund, PJ
粒子滤波器提供了一种通用的数值工具来近似非线性和非高斯滤波问题中状态的后验密度函数。虽然粒子滤波器相当容易实现和调整，但其主要缺点是它是计算机密集型的，计算复杂性随着状态维度的增加而迅速增加。解决这个问题的一种方法是边缘化动态中线性出现的状态。结果是一个卡尔曼滤波器与每个粒子相关联。本文的主要贡献是推导了一般非线性状态空间模型的边缘化粒子滤波器的细节。还将讨论典型信号处理应用中发生的几个重要的特殊情况。边缘化粒子滤波器应用于飞机组合导航系统。事实证明，完整的高维系统可以基于对除三个状态之外的所有状态使用边缘化的粒子滤波器。据报道，在实际飞行数据上表现出色。
The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. The main contribution in this paper is the derivation of the details for the marginalized particle filter for a general nonlinear state-space model. Several important special cases occurring in typical signal processing applications will also be discussed. The marginalized particle filter is applied to an integrated navigation system for aircraft. It is demonstrated that the complete high-dimensional system can be based on a particle filter using marginalization for all but three states. Excellent performance on real flight data is reported.