A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion

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Abstract

Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU. The QGRU code is available at https://github.com/onyekpeu/Quarternion-Gated-Recurrent-Unit.
Original languageEnglish
Article number117
Number of pages16
JournalInformation (Switzerland)
Volume12
Issue number3
DOIs
Publication statusPublished - 9 Mar 2021

Bibliographical note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords

  • gated recurrent unit
  • quaternion neural network
  • quaternion gated recurrent unit
  • human activity recognition
  • INS
  • GPS outage
  • autonomous vehicle navigation
  • inertial navigation
  • neural networks
  • Autonomous vehicle navigation
  • Gated recurrent unit
  • Human activity recognition
  • Quaternion gated recurrent unit
  • Neural networks
  • Quaternion neural network
  • Inertial navigation

ASJC Scopus subject areas

  • Information Systems

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