@inproceedings{0cb80d9fc90b49d890b643f570f1cb95,
title = "A fusion method based on ANN to overcome the GNSS Outages for GNSS/INS System",
abstract = "The performance of the GNSS/INS integrated navigation system will be reduced during GNSS outages. In order to deal with GNSS outages, a fusion method based on ANN is proposed. First, the ELM network is used to achieve rapid detection of the sudden outages of the GNSS signal. Then, according to the output of the INS system, the BP neural network is used to predict the incremental output of GNSS, and the accuracy is improved by adopting the past information of the INS in different steps.This method has been verified in actual vehicle data experiments.The accuracy of the ELM detection method was tested through comparative experiments, and the improvement of navigation accuracy based on the improved BPNN fusion method during GNSS outages.Comprehensive experiment show that the integrated navigation fusion algorithm based on ELM and BPNN can effectively reduce the navigation error during GNSS signal interruption.At the same time, different algorithm options are provided to balance the computational burden and navigation accuracy.",
keywords = "BP neural network, ELM, GNSS outages, GNSS/INS integrated navigation",
author = "Xuan Xiao and Sun, \{Jia Xing\} and Qin Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 China Automation Congress, CAC 2021 ; Conference date: 22-10-2021 Through 24-10-2021",
year = "2021",
doi = "10.1109/CAC53003.2021.9728318",
language = "English",
series = "Proceeding - 2021 China Automation Congress, CAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4914--4919",
booktitle = "Proceeding - 2021 China Automation Congress, CAC 2021",
address = "United States",
}