A Comparison of Correlation-Agnostic Techniques for Magnetic Navigation

Abstract

Navigation using a Global Navigation Satellite System (GNSS) is common for autonomous vehicles (ground or air). Unfortunately, GNSS-based navigation solutions are often susceptible to jamming, interference, and a limited number of satellites. A proposed technique to aid in navigation when a GNSS-based system fails is magnetic navigation - navigation using the Earth's magnetic anomaly field. This solution comes with its own set of problems including the need for quality magnetic maps in every area in which magnetic navigation will be used. Many of the currently available magnetic maps are generated from a combination of dated magnetic surveys, resulting in maps riddled with spatially correlated errors, the correlation structure of which is largely unknown. The correlations are further confounded while navigating because they depend on how fast a vehicle moves through the map in addition to the original correlated error structure. Traditionally, this spatial correlation has been handled by introducing a First Order Gauss-Markov (FOGM) noise model into the estimation routine, with the FOGM parameters set somewhat arbitrarily. In this paper, we investigate the possibility of using correlation agnostic fusion techniques (i.e., Covariance Intersection and Probabilistically Conservative Fusion) for magnetic navigation. These techniques have the advantage of not requiring any parameter tuning; the same method and tuning parameters are used regardless of the spatial correlation. We demonstrate that utilizing probabilistically conservative fusion leads to navigation results that are better than many tuned approaches and reasonably close to the best possible tuning parameters of a FOGM.