Improving Aeromagnetic Calibration Using Artificial Neural Networks
The Global Positioning System (GPS) has proven itself to be the single most accurate positioning system available, and no navigation suite is found without a GPS receiver. Even basic GPS receivers found in most smartphones can easily provide high quality positioning information at any time. Even with its superb performance, GPS is prone to jamming and spoofing, and many platforms requiring accurate positioning information are in dire need of other navigation solutions to compensate in the event of an outage, be the cause hostile or natural. Indeed, there has been a large push to achieve an alternative navigation capability which performs nearly as well as GPS. One navigation method which has shown promise to increase navigation performance of aircraft utilizes magnetic anomalies[1] local variations in the Earth's crust to discern position. One significant drawback to this approach is the magnetic disturbance generated by the aircraft itself, which must be accounted for and eliminated. Current calibration procedures involve placing the magnetometer on a long stinger far from the aircraft body to minimize interference with the magnetic anomaly signal. While some aircraft permit the addition of stingers, many do not. No calibration procedure exists which satisfies potential location restraints of the magnetometer and the calibration problem for these less ideal aircraft, especially potentially magnetically noisy platforms such as an F16. Current linear models which attempt to correct mild disturbance fields on more ideal aircraft exist. We propose that a more sophisticated model is necessary to make magnetic navigation platform agnostic. Specifically, we show that a deep learning approach and the utilization of more inputs than the current de facto calibration procedure known in the literature as Tolles Lawson can achieve a 90% reduction in platform based magnetic disturbance signals.