Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME
Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME
复制标题
数据关联 — 解决方案或避免：基于 RFS 框架和 SME 因子图的过滤器评估
DOI：
10.1109/mfi.2017.8170349
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Knoll
中科院分区：
文献类型：
--
作者：
Dhiraj Gulati;Uzair Sharif;Feihu Zhang;Daniel Clarke;A. Knoll
数据或测量-跟踪关联是执行多目标多传感器协同定位（CL）以获得更好的状态估计的任何解决方案中不可或缺且昂贵的一部分。在各种最先进的解决方案之间进行了各种性能评估，但它们通常受限于同一类算法。然而，在多目标多传感器环境下，已有解决方案可以避免数据关联任务来执行CL。利用对称测量方程（SMEs）的因子图就是这样一种解决方案。在本文中，我们比较和对比了使用最先进的随机有限集（RFS）方法和使用中小企业的因子图解决方案的状态估计。对于RFS，我们使用了多传感器多目标和广义标记多伯努利（GLMB）滤波器。这两种解决方案使用了概念上不同的方法，GLMB Filter隐式地解决了数据关联，而基于因子图的解决方案完全避免了这个任务。仿真显示了一个有趣的结果，在简单的场景中实现的GLMB滤波器可以有效地执行。但当传感器误差增加时，GLMB滤波器的性能比使用sme的因子图下降得更快。
Data or measurement-to-track association is an integral and expensive part of any solution performing multi-target multi-sensor Cooperative Localization (CL) for better state estimation. Various performance evaluations have been performed between various state-of-the-art solutions, but they have been often limited within same family of algorithms. However, there exist solutions which avoid the task of data association to perform the CL in a multi-target multi-sensor environment. Factor Graphs using Symmetric Measurement Equations (SMEs) factor is one such solution. In this paper we compare and contrast the state estimation using state-of-the-art Random Finite Set (RFS) approach and using a Factor Graph solution with SMEs. For a RFS we use multi-sensor multi-object with the Generalized Labeled Multi-Bernoulli (GLMB) Filter. These two solution use conceptually different approaches, GLMB Filter solves the data association implicitly, but Factor Graph based solution avoids the task altogether. Simulations present an interesting results where for simple scenarios implemented GLMB filter performs efficiently. But the performance of GLMB Filter degrades faster than Factor Graphs using SMEs when the error in the sensors increase.
影响因子：
5.4
作者：
Reuter, Stephan;Vo, Ba-Tuong;Dietmayer, Klaus
通讯作者：
Dietmayer, Klaus