Information-Aware Guidance for Magnetic Anomaly based Navigation

TL;DR

This paper presents two innovative magnetic anomaly-based navigation methods that improve autonomous vehicle localization accuracy without GPS by reducing uncertainty through information-driven guidance laws, validated in simulations and hardware tests.

Contribution

It introduces two novel information-aware guidance techniques for magnetic anomaly navigation, enhancing localization accuracy without GPS in autonomous systems.

Findings

Both methods effectively reduce localization uncertainty.

Guidance laws improve navigation accuracy in simulations.

Hardware experiments confirm the approaches' practicality.

Abstract

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details the use of two navigation systems for autonomous agents using magnetic field anomalies to localize themselves within a map; both techniques use the information content in the environment in distinct ways and are aimed at reducing the localization uncertainty. The first method is based on a nonlinear observability metric of the vehicle model, while the second is an information theory based technique which minimizes the expected entropy of the system. These conditions are used to design guidance laws that minimize the localization uncertainty and are verified both in simulation and hardware experiments are presented for the observability approach.