State Estimation for GPS Outage Based on Improved Nonlinear Autoregressive Model

Xiaoran Zhang, Yuting Bai, Senchun Chai

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

4 Citations (Scopus)

Abstract

The accurate and immediate state estimation is essential in control of navigation system. The traditional Global Position System/ Inertial Navigation System (GPSIINS) integration may be invalid without location information provided by GPS during outage periods. A state estimation framework is proposed in this paper to obtain GPS location information during satellite outages. Firstly, a Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX) is designed to characterize the outage periods. Secondly, the integration of Least Square Support Vector Machine (LSSVM) with NARMAX is implemented by using LSSVM to identify NARMAX parameters. The model is trained at first based on present planar angular information and historical location increment feedback provided by GPS with estimation error feedback before outage periods. It switches to predictor mode during outage periods without GPS information. Also, time-serial data are analyzed in NARMAX-LSSVM model to excavate the data features in time dimension. An experiment was conducted to verify the proposed model with multi-step prediction. The results were compared with other traditional methods to show its improvement and validation in state estimation.

Original languageEnglish
Title of host publicationICSESS 2018 - Proceedings of 2018 IEEE 9th International Conference on Software Engineering and Service Science
EditorsLi Wenzheng, M. Surendra Prasad Babu
PublisherIEEE Computer Society
Pages840-843
Number of pages4
ISBN (Electronic)9781538665640
DOIs
Publication statusPublished - 2 Jul 2018
Event9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018 - Beijing, China
Duration: 23 Nov 201825 Nov 2018

Publication series

NameProceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS
Volume2018-November
ISSN (Print)2327-0586
ISSN (Electronic)2327-0594

Conference

Conference9th IEEE International Conference on Software Engineering and Service Science, ICSESS 2018
Country/TerritoryChina
CityBeijing
Period23/11/1825/11/18

Keywords

  • GPS Outage
  • Least Square Support Vector Machine
  • Nonlinear autoregressive model
  • State Estimation

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