A. R. Gnadt et al., “Signal Enhancement for Magnetic Navigation Challenge Problem,” arXiv, pp. 1–12, 2023, doi: 10.48550/arXiv.2007.12158.

A. R. Gnadt, A. B. Wollaber, and A. P. Nielsen, “Derivation and Extensions of the Tolles-Lawson Model for Aeromagnetic Compensation,” arXiv, pp. 1–9, 2022, doi: 10.48550/arXiv.2212.09899.

A. R. Gnadt, “Advanced Aeromagnetic Compensation Models for Airborne Magnetic Anomaly Navigation,” Massachusetts Institute of Technology, 2022. Available:  https://dspace.mit.edu/handle/1721.1/145137.

A. R. Gnadt, “Machine Learning-Enhanced Magnetic Calibration for Airborne Magnetic Anomaly Navigation,” in AIAA SCITECH 2022 Forum, 2022, pp. 1–16, doi: 10.2514/6.2022-1760.

Recommended Reading

S. H. Bickel, “Small Signal Compensation of Magnetic Fields Resulting from Aircraft Maneuvers,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-15, no. 4, pp. 518–525, 1979, doi: 10.1109/TAES.1979.308736.

A. J. Canciani, “Absolute Positioning Using the Earth’s Magnetic Anomaly Field,” Air Force Institute of Technology, 2016. Available: https://scholar.afit.edu/etd/251/.

A. J. Canciani and J. F. Raquet, “Absolute Positioning Using the Earth’s Magnetic Anomaly Field,” Navigation, vol. 63, no. 2, pp. 111–126, 2016, doi: 10.1002/navi.138.

A. J. Canciani and J. F. Raquet, “Airborne Magnetic Anomaly Navigation,” IEEE Trans. Aerosp. Electron. Syst., vol. 53, no. 1, pp. 67–80, 2017, doi: 10.1109/TAES.2017.2649238.

A. J. Canciani, “Magnetic Navigation on an F-16 Aircraft using Online Calibration,” IEEE Trans. Aerosp. Electron. Syst., pp. 1–15, 2021, doi: 10.1109/TAES.2021.3101567.

K. A. Emery, “Modeling Aircraft Disturbance Fields for Magnetic Navigation Using Dense ANNs and the Novel MANNTL Architecture,” Air Force Institute of Technology, 2021. Available: https://scholar.afit.edu/etd/4894/.

M. C. Hezel, “Improving Aeromagnetic Calibration Using Artificial Neural Networks,” Air Force Institute of Technology, 2020. Available: https://scholar.afit.edu/etd/3589/.

M. Ma, D. Cheng, S. Chalup, and Z. Zhou, “Uncertainty Estimation in the Neural Model for Aeromagnetic Compensation,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 12, pp. 1942–1946, 2018, doi: 10.1109/LGRS.2018.2864239.

P. M. Williams, “Aeromagnetic Compensation using Neural Networks,” Neural Comput. Appl., vol. 1, no. 3, pp. 207–214, 1993, doi: 10.1007/BF01414949.

X. Xu, L. Huang, X. Liu, and G. Fang, “DeepMAD: Deep Learning for Magnetic Anomaly Detection and Denoising,” IEEE Access, vol. 8, pp. 121257–121266, 2020, doi: 10.1109/ACCESS.2020.3006795.

P. Yu, X. Zhao, and J. Jiao, “Aeromagnetic Data Preprocessing Method Based on Deep Learning,” in AGU Fall Meeting Abstracts, 2019, [Online]. Available: https://ui.adsabs.harvard.edu/abs/2019AGUFMNS13B0664Y.