@inproceedings{d25923c9a81b4316a6534537900c59d2,
title = "Fault detection method of integrated navigation based on LVQ neural network",
abstract = "In the present study, a GPS/CNS/SINS federated filter model is proposed firstly to improve the low accuracy of fault detection in multi-sensor integrated navigation system. On this basis, an LVQ neural network assisted integrated navigation fault detection method is developed for LVQ (Learning Vector Quantization) networks with few design parameters, simple network structure and non-normalized input vectors during usage. The optimal number of neurons in the competitive layer is determined by K-CV (Cross Validation) verification method, and LVQ neural network is used to identify and classify the soft and hard faults added at different times. The simulation results indicate that compared with traditional neural network, LVQ neural network achieves higher detection accuracy (93%) with lower CPU usage. Thus, it is convinced that the study has great engineering significance and practical value.",
keywords = "Fault detection, LVQ neural network, federated filtering, integrated navigation system",
author = "Xiaojing Du and Changte Sun and Huaijian Li and Rongjing Xu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/DOCS55193.2022.9967752",
language = "English",
series = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022",
address = "United States",
}