Sequence matching techniques for GNSS-space based navigation enhancement

This poster is situated in the field of Global Navigation Satellite Systems (GNSS), with a specific focus on the “Lunar GNSS Receiver Experiment” (LuGRE). LuGRE, a pioneering joint venture between the National Aeronautics and Space Administration (NASA) and the Agenzia Spaziale Italiana (ASI), that demonstrated positioning, navigation, and timing (PNT) capabilities on the lunar surface. While GNSS technologies have been primarily developed and optimized for terrestrial applications, the GNSS signal also propagates beyond the Earth, making it potentially useful for space segment applications, including deep space missions. However, the use of GNSS technology in deep space presents significant challenges due to the harsh environment, where the signal is often buried in noise and subject to numerous non-ideal conditions. Currently, space vehicle navigation heavily relies on ground segment assets, such as Radio Frequency (RF) tracking through Deep Space Networks (DSNs) and Direct-to-Earth (DTE) links. These assets enable precise Orbit Determination (OD) trough complex off-board processing algorithms. Alternatively, ground-based observations can be combined with on-board measurements for semi-autonomous navigation. However, this reliance on ground-based assets has drawbacks, including escalating operational costs, the impracticality of managing multiple missions due to resource depletion, and communication delays that can affect latency-critical operations. In light of the forthcoming deep-space exploration roadmap, there is an urgent need to enhance spacecraft autonomy. Within the Space Service Volume (SSV), GNSSs are seen as a crucial asset for this purpose. This work explores the use of Sequence Matching techniquesintegration of an aiding trajectory within the PVT module as part of the framework for autonomous orbital navigation based on GNSS systems. Specifically, it addresses a custom Bayesian filtering architecture using a kinematic approach, optimally integrating aiding observations which can be available onboard thanks to additional subsystems or as preloaded mission trajectory information. The integration occurs after the solution of a sequence matching problem between the asynchronous aiding observations and the GNSS solution. The development of this work involved extensive use of multiphysics simulations on LuGRE mission data and a thorough literature review to design and fine-tune an innovative matching approach. Ultimately, this technology could have far-reaching implications in various markets, including autonomous driving, railway safety and monitoring, agriculture, and more. This work represents a significant step forward in the innovative application of GNSS technology for deep space exploration.