Graphical Approach to Representation and Inference in Multi-sensor State Estimation

Xin Zhang and Xingqun Zhan

Abstract:	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.
Published in:	Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017) September 25 - 29, 2017 Oregon Convention Center Portland, Oregon
Pages:	2603 - 2611
Cite this article:	Zhang, Xin, Zhan, Xingqun, "Graphical Approach to Representation and Inference in Multi-sensor State Estimation," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 2603-2611. https://doi.org/10.33012/2017.15283
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