A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion

Uche Onyekpe, Vasile Palade, Stratis Kanarachos, Stavros Richard G. Christopoulos

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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 re-quire a stronger mapping of their features to further enhance the prediction accuracy. A novel Qua-ternion 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-dependen-cies 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)
Issue number3
Publication statusPublished - 1 Mar 2021
Externally publishedYes


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