A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion

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


Autonomous underwater vehicles (AUV) are commonly used in many underwater applications. Usually, inertial sensors and Doppler velocity log readings are used in a nonlinear filter to estimate the AUV navigation solution. The process noise covariance matrix is tuned according to the inertial sensors' characteristics. This matrix greatly influences filter accuracy, robustness, and performance. A common practice is to assume that this matrix is fixed during the AUV operation. However, it varies over time as the amount of uncertainty is unknown. Therefore, adaptive tuning of this matrix can lead to a significant improvement in the filter performance. In this work, we propose a learning-based adaptive velocity-aided navigation filter. To that end, handcrafted features are generated and used to tune the momentary system noise covariance matrix. Once the process noise covariance is learned, it is fed into the model-based navigation filter. Simulation results show the benefits of our approach compared to other adaptive approaches.

Original languageEnglish
Title of host publicationOCEANS 2022 Hampton Roads
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665468091
StatePublished - 2022
Event2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States
Duration: 17 Oct 202220 Oct 2022

Publication series

NameOceans Conference Record (IEEE)
ISSN (Print)0197-7385


Conference2022 OCEANS Hampton Roads, OCEANS 2022
Country/TerritoryUnited States
CityHampton Roads

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Autonomous underwater vehicles
  • Deep Neural Network
  • Handcrafted features
  • Inertial Measurement Unit
  • Inertial Navigation System
  • Kalman Filter
  • Machine Learning
  • Supervised Learning
  • Tracking

ASJC Scopus subject areas

  • Oceanography
  • Ocean Engineering


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