DOAJ Open Access 2023

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks

Elkyn Alexander Belalcazar-Bolaños Diego Torricelli José L. Pons

Abstrak

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the cases, which represents a significant improvement over current threshold-based detection algorithms.

Topik & Kata Kunci

Chemical technology

Penulis (3)

Elkyn Alexander Belalcazar-Bolaños

Diego Torricelli

José L. Pons

Format Sitasi

APA MLA BibTeX

Belalcazar-Bolaños, E.A., Torricelli, D., Pons, J.L. (2023). Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks. https://doi.org/10.3390/s23249683

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.3390/s23249683
Akses: Open Access ✓