Magnetic Positioning Based on Evolutionary Algorithms

The spatially discernible indoor magnetic field indicates locations through different magnetic readings at various positions. Therefore, magnetic positioning has garnered attention due to its promising localization accuracy and infrastructure-free nature, significantly reducing the investment in localization. Since the magnetic field covers all indoor environments, magnetic positioning holds the potential to create a ubiquitous indoor positioning system. This chapter investigates the stability of the magnetic field concerning factors such as devices, testers, materials, and dates. Compensation methods for different types of magnetic features are studied based on fluctuation patterns to achieve accurate positioning results. Evolutionary algorithm-based optimization strategies are proposed for online localization, tailored to the types of used magnetic features. Testing experiments validate the feasibility and efficiency of utilizing evolutionary algorithms to enhance magnetic positioning performance.