Calibration of low-cost triaxial magnetometer

Kuncar, Ales; Sysel, Martin; Urbánek, Tomáš

doi:10.1051/matecconf/20167605008

Cited by 4 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Correct coordinate system axis alignment and suitable calibration of tri-axial magnetometry data [cf. 143] are crucial pre-processors, without which, heading estimates would likely incorporate substantial error [cf 18,143]…”

mentioning

confidence: 99%

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Gunner

Holton

Scantlebury

et al. 2021

Preprint

View full text Add to dashboard Cite

Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). Yet relatively few bio-logging studies have adopted this approach due to an apparent inaccessibility of the complex analytical processes involved. We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully-annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the supplementary information as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air, respectively. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

show abstract

mentioning

confidence: 99%

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Gunner

Holton

Scantlebury

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…149] are crucial pre-processors, without which, heading estimates would likely incorporate substantial error [cf. 21,149]. The tiltcompensated compass method described below (following the framework outlined by Pedley [21]), requires the aerospace (x-North, y-East, z-Down) (right-handed) coordinate system, or 'NED' (cf.…”

Section: Preparing the Three Axes Of Rotation For Derivation Of Headingmentioning

confidence: 99%

“…Multiple mathematically sophisticated algorithms have been developed to correct distortions from each magnetometer channel's output [e.g., 23,149,150,151,152]. We provide an annotated R script-Gundog.…”

Section: Preparing the Three Axes Of Rotation For Derivation Of Headingmentioning

confidence: 99%

Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks

et al. 2021

View full text Add to dashboard Cite

Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

show abstract

“…In this research, the algorithm proposed by Kuncar, Sysel and Urbanek () is used for the calibration. This algorithm leads to identifying 12 unknown parameters for the compensation of HIE and SIE effects.…”

Section: Calibrationmentioning

confidence: 99%

Comparison of a low‐cost magneto‐inductive magnetometer with a proton magnetometer: a case study on the Galali iron ore deposit in western Iran

Shahsavani

2018

Near Surface Geophysics

View full text Add to dashboard Cite

A B S T R A C TThe proton magnetometers, also known as the proton precession magnetometers, are among the most widely used instruments for magnetometry surveys, owing to their high resolution which is about 0.01 nT. As the economically attractive ferromagnetic deposits generate a magnetic field anomaly which exceeds 100 nT, this resolution is not needed for detecting such large signals. The recently introduced magneto-inductive technology has led to the development of a low-cost magnetometer that can measure the ground magnetic field up to a resolution of around 10 nT. These magnetometers are inexpensive, come in a very small size, and are lightweight compared to the more common magnetometers such as the proton precession type. In this research, a low-cost magneto-inductive sensor and a highly precise proton magnetometer are simultaneously utilized at the same profiles on the Galali iron ore deposit in the northwest of Iran. The discrepancy of two measurements is less than 400 nT, often less than 100 nT, while the changes in the total magnetic field on this anomaly are around 7500 nT, which is 11 times larger than the maximum difference between the two measurements. In addition, the value of the regression coefficient between the two measurements for all profiles is more than 0.97. Also, the slope of the fitted line for the two measurements for all profiles is close to one. These results demonstrate the applicability of the new low-cost magneto-inductive sensor in magnetic prospecting.

show abstract

Calibration of low-cost triaxial magnetometer

Cited by 4 publications

References 5 publications

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks

Comparison of a low‐cost magneto‐inductive magnetometer with a proton magnetometer: a case study on the Galali iron ore deposit in western Iran

Contact Info

Product

Resources

About