Research on Height Constraint Algorithm Based on Hidden Markov Model

Ruirong Wang, Chunlei Song, Chenchen Wei, Pei Yu

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

2 Citations (Scopus)

Abstract

Indoor pedestrian navigation has recently become an important field of interest in satellite-denied scenarios. Zero Velocity Update prevents an accumulated error growth caused by the noise of MEMS-IMU. However, the height error is still an issue and accumulates over time. We propose a height constraint algorithm based on Hidden Markov Model and Recursive Viterbi algorithm without any other sensors besides shoe-mounted IMU. The presented algorithm addresses the issue of setting the height reference threshold because of the unfixed height change value of pedestrian when climbing stairs. And we propose a simple method to fetch height state without complex gait phase detection. For the assessment of the performance of the proposed height constraint, we compare the height error estimated with and without the proposed algorithm. The experimental results show that the height constraint algorithm can reduce height error within 0.1 meters with preferable stability and robustness at the same time.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
PublisherIEEE Computer Society
Pages3275-3280
Number of pages6
ISBN (Electronic)9789881563903
DOIs
Publication statusPublished - Jul 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: 27 Jul 202029 Jul 2020

Publication series

NameChinese Control Conference, CCC
Volume2020-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference39th Chinese Control Conference, CCC 2020
Country/TerritoryChina
CityShenyang
Period27/07/2029/07/20

Keywords

  • Hidden Markov Model
  • Recursive Viterbi
  • height constraint
  • indoor pedestrian navigation

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