Aeromagnetic attitude compensation for uninhabited aircraft systems without high-altitude calibration patterns using hybrid recurrent neural networks
Bibliographic record
Abstract
Abstract Since the 1950s, Tolles-Lawson-based aeromagnetic compensation methods have been used to separate an aircraft's magnetic signal from signal associated with ground geologic and cultural features. This is done by performing a high-altitude figure-of-merit (FOM) flight and fitting the band-pass-filtered magnetic data to determine compensation parameters. This paper describes a supervised hybrid recurrent neural network (HRNN) algorithm trained on low-altitude survey data to perform aeromagnetic compensation. The proposed HRNN attitude compensation method can be employed for aeromagnetic surveys where traditional FOM and compensation are not possible. It has particular relevance for surveying via uninhabited aircraft systems (UAS). Firstly, the HRNN was tested on data from a fixed-wing airplane survey, and the results were compared to hardware-based compensation results. The standard deviation of the difference between the two methods for magnetic attitude correction (MAC) was 0.1 nT for the training region and 0.4 nT for the application region, respectively. Secondly, a UAS FOM flight at the highest permitted altitude in Canada, 120 m above ground level, showed similar improvement ratios for software-based least squares (LS) and the proposed HRNN algorithm of 3.5 and 2.6, respectively. The percent change and deviation in differences in MACs from LS to HRNN was 0.0% and 0.9 nT across small-box loops and –2.7% and 0.4 nT across large-box loops. Finally, LS and the proposed HRNN algorithm were applied to a 50 m altitude UAS data set for which no FOM flight was possible. LS did not successfully model aircraft noise, whereas the HRNN demonstrated effective removal of the magnetic signal due to aircraft attitude variations. The modeled HRNN MAC had a standard deviation of 2.4 nT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".