用于有效非线性状态估计的密度树

Henning P. Eberhardt, Vesa Klumpp, U. Hanebeck
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引用次数: 20

摘要

本文提出了一类新的基于特殊空间划分结构的非线性贝叶斯估计——广义八叉树。这种结构最小化了内存和计算开销。它被用作一组节点函数的容器框架,这些节点函数可以分段地近似密度。所有必要的操作都以一种非常通用的方式推导出来,以便允许多种贝叶斯估计。所提出的估计器特别适合于多模态非线性估计问题。首先从理论上分析了所得到的估计器的运行时性能,然后通过仿真进行了验证。所有的操作在树节点的数量上都有一个线性的运行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Density trees for efficient nonlinear state estimation
In this paper, a new class of nonlinear Bayesian estimators based on a special space partitioning structure, generalized Octrees, is presented. This structure minimizes memory and calculation overhead. It is used as a container framework for a set of node functions that approximate a density piecewise. All necessary operations are derived in a very general way in order to allow for a great variety of Bayesian estimators. The presented estimators are especially well suited for multi-modal nonlinear estimation problems. The running time performance of the resulting estimators is first analyzed theoretically and then backed by means of simulations. All operations have a linear running time in the number of tree nodes.
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