Fault detection method of integrated navigation based on LVQ neural network

Xiaojing Du*, Changte Sun, Huaijian Li, Rongjing Xu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665459822
DOIs
Publication statusPublished - 2022
Event4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022 - Chengdu, China
Duration: 28 Oct 202230 Oct 2022

Publication series

Name2022 4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

Conference

Conference4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022
Country/TerritoryChina
CityChengdu
Period28/10/2230/10/22

Keywords

  • Fault detection
  • LVQ neural network
  • federated filtering
  • integrated navigation system

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