Luka Didoszak
Research Mentor(s): Mark Moldwin
Mentor Department: Department of Climate and Space Sciences and Engineering
Authors: Luka Didoszak, Mark Moldwin
Session: Session 1 (9:00am – 9:50am)
Presentation Type: Poster 84
Abstract
Magnetometers are highly sensitive instruments that collect precise data on the magnetic field around them. However, when surveying large areas using drones, the drone’s own magnetic components can interfere with the desired measurements. A potential way around this is to characterize these signatures from the drone’s components and filter them out from the signal that is collected. Looking for specific frequencies in the magnetic field emitted from the drone could potentially allow for filtering the signals from the data. To find these frequencies our research utilized four RM3100 magnetometers connected together, creating a Quad-Mag. The Quad-Mag is connected to a Raspberry Pi to allow for data storage and initiation of test results. The nature of the magnetic field’s frequencies is unknown, which led to us testing the drone in different flying conditions to see if it impacted the outputted magnetic field. With the help of power spectral density (PSD) graphs different frequencies were pulled out and their respective power visualized. Comparing the PSDs of the drone off and in flight mode conditions show the difference and highlighted distinct frequencies. The collected data can be tested with the cleaning algorithm being developed to filter out the data in real-world uses. This method could be further scaled up and used on spacecraft. Current magnetometers in space are attached to large vulnerable boom sticks to create separation and reduced noise from the spacecraft’s magnetic field. Being able to filter out the field would allow for boom-less satellites and increase the longevity of magnetic measuring systems in space. This would also increase reliability and save on costly repairs/new equipment.
