Over the past few decades, the paradigm of geomagnetic navigation has been under consideration as a potential alternative to GPS in situations where GPS signals are subject to interference or are inaccessible. However, navigation methodologies that rely on magnetic fields continue to face practical development challenges due to their complex characterization, which has so far resulted in modest localization performance compared to GPS. This article presents the design and development of an innovative intelligent geomagnetic-based framework for navigation within GPS-denied environments. Two artificial-intelligence-based concepts, i.e., generative adversarial networks and deep reinforcement learning, are integrated within the framework with a Rao–Blackwellized particle filter in an attempt to introduce the onboard intelligence necessary to perform navigation using earth's magnetic anomalies with an acceptable performance. This article includes a detailed formulation of the proposed framework, its components, and demonstration of selected key capabilities through numerical simulations. The results show the promising characteristics of the proposed navigation strategy and the potential to bridging the gap that separates geomagnetic-based navigation from practical applications and deployment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Artificial-Intelligence-Assisted Geomagnetic Navigation Framework


    Contributors:


    Publication date :

    2025-04-01


    Size :

    5577814 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Artificial Intelligence in Navigation Systems

    Swiatnicki, Z. / Royal Institute of Navigation | British Library Conference Proceedings | 1993



    Nonlinear filtering techniques for geomagnetic navigation

    Guo, Caifa / Cai, Hong / Hu, Zhengdong | SAGE Publications | 2014


    Geomagnetic navigation of near-Earth spacecraft

    Balashova, N.N. / Barankov, P.A. / Maslov, V.D. et al. | Tema Archive | 1991