10.33012/2025.20278">
 

Document Type

Conference Proceeding

Publication Date

9-2025

Abstract

Magnetic Anomaly Navigation (MagNav) is a map-based method of navigation which relies on accurately obtaining the anomaly field to a high level of precision to achieve good navigation results. This requires utilizing high quality sensors, accurately modeling disturbance fields from the aircraft & other sources, and creating high-fidelity maps. Aeromagnetic survey data or marine track survey data must be processed into a product that can be used as a reference for a magnetic navigator and a variety of techniques can be utilized for this processing. The data collection for different survey types reflects choices typically made to study the underlying geology. Survey design choices may include survey line spacing, diurnal correction, or careful selection of data over a larger period of time. These choices may highlight features to geoscientists but for navigation purposes the final product must represent the actual magnetic field that would be seen by a sensor used for navigation in order to achieve good map matching. In this research, we address how the quality of the magnetic survey data and the data processing techniques used impact the quality of the navigation solution using real flight data and maps generated from several sources. These sources include one modern high-quality aeromagnetic survey that represents a fully sampled set of measurements, one low resolution marine survey which contains under-sampled data, and one set of marine tracks from the NOAA/NCEI database spanning a wide range of years and data quality. The magnetic anomaly grids were produced using several different gridding techniques that capture different features of the underlying magnetic field. We used these maps as navigational inputs to recent flight data with 200-400 km long legs and an extended Kalman filter designed using Inertial Navigation Utilities (INU) (Woodburn 2024). Data from a total of four separate flight legs are utilized for this analysis. We utilize the DRMS metric and introduce an exceedance curve metric to summarize the navigation performance for each test. We show how the quality of the survey data can significantly impact navigation performance and introduce a method for factoring in the uncertainty of a magnetic map into the navigation algorithm to improve filter performance and stability.

Comments

This conference paper is accessible from the publisher through purchase or ION membership, using the DOI link below.

As the repository of the employer of co-authors Nielsen and Blakeley, AFIT Scholar provides current AFIT students, faculty and staff with access to this paper through email requests to the AFIT Scholar Administrator: afit.enwl.repository@us.af.mil.  Please paste the citation below and MS 3947 into your request.

Source Publication

Proceedings of the 38th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2025)

Download

Share

COinS