Adaptive Step Size Learning With Applications to Velocity Aided Inertial Navigation System

Barak Or, Itzik Klein

Research output: Contribution to journalArticlepeer-review

Abstract

Autonomous underwater vehicles (AUV) are commonly used in many underwater applications. Recently, the usage of multi-rotor unmanned autonomous vehicles (UAV) for marine applications is receiving more attention in the literature. Usually, both platforms employ an inertial navigation system (INS), and aiding sensors for an accurate navigation solution. In AUV navigation, Doppler velocity log (DVL) is mainly used to aid the INS, while for UAVs, it is common to use global navigation satellite systems (GNSS) receivers. The fusion between the aiding sensor and the INS requires a definition of step size parameter in the estimation process. It is responsible for the solution frequency update and, eventually, its accuracy. The choice of the step size poses a tradeoff between computational load and navigation performance. Generally, the aiding sensors update frequency is considered much slower compared to the INS operating frequency (hundreds Hertz). Such high rate is unnecessary for most platforms, specifically for low dynamics AUVs. In this work, a supervised learning based adaptive tuning scheme to select the proper INS step size is proposed. To that end, a velocity error bound is defined, allowing the INS/DVL or the INS/GNSS fusion filter to act in a sub-optimal working conditions, and yet minimize the computational load. Results from simulations and field experiment show the benefits of using the proposed approach. In addition, the proposed framework can be applied to any other fusion scenarios between any type of sensors or platforms.

Original languageEnglish
Pages (from-to)85818-85830
Number of pages13
JournalIEEE Access
Volume10
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Autonomous underwater vehicles
  • Inertial navigation
  • Kalman filtering
  • Machine learning
  • Step size
  • Supervised learning
  • Unmanned aerial vehicles

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)
  • Electrical and Electronic Engineering

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