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  • 标题:Robust and Reliable Multi-Sensor Navigation Filter for Maritime Application
  • 本地全文:下载
  • 作者:J.J. Gehrt ; S. Liu ; M. Nitsch
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:14482-14487
  • DOI:10.1016/j.ifacol.2020.12.1450
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis publication describes the further development of a navigation concept especially designed for maritime application and its recent integration on the research ship DENEB of the German Federal Maritime and Hydrographic Agency (BSH). The proposed navigation concept consists of a tightly-coupled navigation filter, which bases on quantities of an inertial measurement unit (IMU), is aided by a Doppler velocity log (DVL), and Global Navigation Satellite System (GNSS) dual constellation signals of two antennas. Integrity monitoring with fault detection and exclusion (FDE), ensures the reliability of the GNSS observables. A new approach for integrating low quality and biased DVL data without endangering the state estimation accuracy and preciseness, but enhancing it’s robustness is introduced. A new integration of real-time sea level data from an online service in the filter improves robustness in addition. The navigation system is evaluated in an extensive measurement campaign with DENEB in harbor of Rostock, Germany. Basically, two different advantages of the proposed navigation concept are investigated. Firstly, evaluation proves that integration of the low quality and biased data of the vessels DVL is possible without lowering the navigation filter accuracy significantly. Secondly, the robustness of the concept against sensor failure is shown. Therefore, by means of post-processing the recorded data, GNSS and DVL outage is investigated. Evaluation verifies, that the multi-sensor fusion and the integration of real-time sea level data improves the robustness of the navigation solution and therefore is qualified for autonomous application.
  • 关键词:KeywordsAutonomous vehiclesGlobal positioning systemsInertial navigationKalman filtersMultisensor integrationNavigation systemsSensorfusion
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