IMU Data for Pitch and Roll Compensation and Vertical Acceleration Estimation: A Machine Learning Approach for Strapdown Gravimeters

2026-11-01T02:11:52Z https://oai.datacite.org/oai

doi:10.17632/crvy9jgb6g.1 2025-01-20T13:20:59Z ELSEVIER BL.MENDELEY

1 BL.MENDELEY 10.17632/CRVY9JGB6G.1 Iafolla, Lorenzo Lorenzo Iafolla 0000-0001-5584-2318 Istituto Nazionale di Geofisica e Vulcanologia Chiappini, Massimo Massimo Chiappini 0000-0001-7433-9435 Istituto Nazionale di Geofisica e Vulcanologia Carluccio, Roberto Roberto Carluccio 0000-0003-4344-0965 Istituto Nazionale di Geofisica e Vulcanologia Chiappini, Stefano Stefano Chiappini 0000-0001-9520-7618 Istituto Nazionale di Geofisica e Vulcanologia Fiorenza, Emiliano Emiliano Fiorenza 0000-0001-7634-2152 Istituto di Astrofisica e Planetologia Spaziali Lefevre, Carlo Carlo Lefevre 0000-0003-0649-9941 Istituto di Astrofisica e Planetologia Spaziali Loffredo, Pasqualino Pasqualino Loffredo 0000-0003-4657-1119 Istituto di Astrofisica e Planetologia Spaziali Lucente, Marco Marco Lucente 0000-0002-5359-5437 Istituto di Astrofisica e Planetologia Spaziali Morbidini, Alfredo Alfredo Morbidini 0000-0002-4189-4616 Istituto di Astrofisica e Planetologia Spaziali Pignatelli, Alessandro Alessandro Pignatelli 0000-0002-3172-2044 Istituto Nazionale di Geofisica e Vulcanologia Santoli, Francesco Francesco Santoli 0000-0003-2493-0109 Istituto di Astrofisica e Planetologia Spaziali IMU Data for Pitch and Roll Compensation and Vertical Acceleration Estimation: A Machine Learning Approach for Strapdown Gravimeters Mendeley Data 2024 Dataset Machine Learning Accelerometer Sensor Aeronautical Sensor Istituto di Astrofisica e Planetologia Spaziali https://ror.org/0141xw169 Istituto Nazionale di Geofisica e Vulcanologia https://ror.org/00qps9a02 2024-04-08T15:03:56Z 10.17632/crvy9jgb6g Creative Commons Attribution 4.0 International Strapdown gravimeters are multi-sensor systems capable of providing all necessary data to reconstruct gravity measurements collected onboard a moving platform (e.g., an airplane, a boat, or a submarine). The primary challenge lies in removing from the gravimeter readings the effects of disturbances, such as unaccounted rotations of the moving platform. This dataset was generated using an experimental setup comprising a three-axial gyroscope and a three-axial accelerometer (i.e., an Inertial Measurement Unit, IMU) installed on a “training platform”. The training platform, equipped with three linear actuators acting as feet, aimed to replicate the operational environment of a moving platform, such as a boat. In this version of the training platform, the IMU was subjected to tilt oscillations (pitch and roll) as well as vertical oscillations, with the latter being monitored by three linear encoders attached to the linear actuators. By analyzing the linear encoders data, one can infer the vertical acceleration and, according to the Equivalence Principle of Einstein, utilize it as a proxy for gravity variations to be measured. The key challenge is to reconstruct from the IMU data the vertical acceleration derived from the linear encoders data. The methodologies developed to address this challenge will play a pivotal role in the advancement of a novel type of strapdown gravimeter known as “Gravimetro Aereo INtelligente” (GAIN), which relies on Machine Learning techniques for data processing. Regione Lazio https://ror.org/008hssd09