计算机科学
代表(政治)
推论
人工智能
估计
算法
国家(计算机科学)
图形模型
卡尔曼滤波器
贝叶斯推理
估计员
作者
Xin Zhang,Xingqun Zhan
出处
期刊:Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017)
日期:2017-09-29
卷期号:: 2603-2611
摘要
A graphical approach to design of an IMM-PDAF (Interacting Multiple Model – Probabilistic Data Association Filter) is presented. In particular, a Dynamic Bayesian Network (DBN) is used to model the temporal evolution of an INS/GNSS integration system that will be used in self-driving cars. Conditional independence embedded in the network is utilized to obtain the system and measurement model that will be the basis of prediction and update steps of the IMM-PDAF. Performance evaluations show that the resulting filter produces acceptable Root Mean Squared (RMS) errors in attitude, position and velocity of a car. A further discussion reveals that this approach is a Plug-and-Play All Source Positioning and Navigation (ASPN)-capable candidate.
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