Factor graphs are widely used in robotics, especially for simultaneous localization and mapping, but also in filtering and smoothing, model-based signal processing, artificial intelligence, coding and decoding. They serve as probabilistic models where variable nodes are associated with system states, and factors are associated with measurements.

The factor graphs techniques have been applied to the positioning algorithms in the recent years, with very positive results.

The objective of FASCINATE is to investigate how to exploit Factor Graph Optimization (FGO) for PNT positioning, and to benchmark its performances with respect to the widely used high-accuracy positioning approaches such as Extended Kalman Filter.

To accomplish this, a FGO navigation engine will be developed and integrated into a comprehensive testbed that includes multiple Global Navigation Satellite System (GNSS) receivers, antennas, and an Inertial Measurement Unit (IMU). This testbed, combined with previously recorded data, will be utilized to assess the performance of the FGO through an experimental campaign.