2000 character limit reached
Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks (2401.09631v1)
Published 17 Jan 2024 in cs.LG, cs.SY, eess.SY, physics.comp-ph, and physics.geo-ph
Abstract: Magnetic navigation (MagNav) is a rising alternative to the Global Positioning System (GPS) and has proven useful for aircraft navigation. Traditional aircraft navigation systems, while effective, face limitations in precision and reliability in certain environments and against attacks. Airborne MagNav leverages the Earth's magnetic field to provide accurate positional information. However, external magnetic fields induced by aircraft electronics and Earth's large-scale magnetic fields disrupt the weaker signal of interest. We introduce a physics-informed approach using Tolles-Lawson coefficients for compensation and Liquid Time-Constant Networks (LTCs) to remove complex, noisy signals derived from the aircraft's magnetic sources. Using real flight data with magnetometer measurements and aircraft measurements, we observe up to a 64% reduction in aeromagnetic compensation error (RMSE nT), outperforming conventional models. This significant improvement underscores the potential of a physics-informed, machine learning approach for extracting clean, reliable, and accurate magnetic signals for MagNav positional estimation.
- Tegg Westbrook. The global positioning system and military jamming. Journal of Strategic Security, 12(2):1–16, 2019.
- Gnss spoofing and detection. Proceedings of the IEEE, 104(6):1258–1270, 2016.
- Aaron J. Canciani. Magnetic navigation on an f-16 aircraft using online calibration. IEEE Transactions on Aerospace and Electronic Systems, 58(1):420–434, 2022. doi: 10.1109/TAES.2021.3101567.
- Airborne magnetic anomaly navigation. IEEE Transactions on aerospace and electronic systems, 53(1):67–80, 2017.
- Passive navigation using local magnetic field variations. In Proceedings of the 2006 National Technical Meeting of the Institute of Navigation, pages 770–779, 2006.
- Magslam: Aerial simultaneous localization and mapping using earth’s magnetic anomaly field. Navigation, 67(1):95–107, 2020.
- Federal Aviation Administration. How gps works, n.d. URL https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/techops/navservices/gnss/gps/howitworks. Accessed: November 28, 2023.
- Walter E Tolles and JD Lawson. Magnetic compensation of mad equipped aircraft. Airborne Instruments Lab. Inc., Mineola, NY, Rept, pages 201–1, 1950.
- Paul Leliak. Identification and evaluation of magnetic-field sources of magnetic airborne detector equipped aircraft. IRE Transactions on Aerospace and Navigational Electronics, ANE-8(3):95–105, 1961.
- Magnetometer calibration using kalman filter covariance matrix for online estimation of magnetic field orientation. IEEE transactions on instrumentation and measurement, 63(8):2013–2020, 2014.
- Localization with magnetic field distortions and simultaneous magnetometer calibration. IEEE Sensors Journal, 21(3):3388–3397, 2020.
- G Noriega. Recent advances in aeromagnetic compensation. Proc. 10th Int. Congr. Prospectors Explorers, pages 1–4, 2017.
- Albert Gnadt. Machine learning-enhanced magnetic calibration for airborne magnetic anomaly navigation. In AIAA SciTech 2022 Forum, page 1760, 2022.
- Neural network calibration of airborne magnetometers. In 2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace), pages 37–42. IEEE, 2023.
- Deepmad: Deep learning for magnetic anomaly detection and denoising. IEEE Access, 8:121257–121266, 2020.
- Uncertainty estimation in the neural model for aeromagnetic compensation. IEEE Geoscience and Remote Sensing Letters, 15(12):1942–1946, 2018.
- Mitchell C Hezel. Improving aeromagnetic calibration using artificial neural networks. Air Force Institute of Technology (AFIT), 2020.
- Liquid time-constant networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 7657–7666, 2021.
- Neural ordinary differential equations. Advances in neural information processing systems, 31, 2018.
- Closed-form continuous-time neural networks. Nature Machine Intelligence, 4(11):992–1003, 2022.
- Barriernet: Differentiable control barrier functions for learning of safe robot control. IEEE Transactions on Robotics, 2023.
- Robust flight navigation out of distribution with liquid neural networks. Science Robotics, 8(77):eadc8892, 2023.
- Interpretable spatiotemporal forecasting of arctic sea ice concentration at seasonal lead times. In NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, 2022.
- Efficient edge-ai models for robust ecg abnormality detection on resource-constrained hardware. medRxiv, pages 2023–08, 2023.
- Neural circuit policies enabling auditable autonomy. Nature Machine Intelligence, 2(10):642–652, 2020.
- A transparent window into biology: a primer on caenorhabditis elegans. Genetics, 200(2):387–407, 2015.
- Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
- Signal enhancement for magnetic navigation challenge problem. arXiv preprint arXiv:2007.12158, 2020.
- C Spearman. The proof and measurement of association between two things. The American Journal of Psychology, 15(1):72–101, 1904.
- Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288, 1996.
- Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016.
- Data analysis, including statistics. Handbook of social psychology, 2:80–203, 1968.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
- Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2623–2631, 2019.