Advanced signal processing techniques for GNSS signals quality analysis

Global Navigation Satellite Systems (GNSS) is expected to play a growing role in enhancing the autonomy of space missions by providing precise positioning and timing information to spacecraft. However, when operating at significant distances from Earth, such as for example throughout Moon Transfer Orbits (MTOs), GNSS signals are typically faint. Thus, ensuring the reliable acquisition and tracking of these signals becomes a pressing concern. Given the increasing interest in the use of GNSS technology, an assessment of signal integrity is a prerequisite for its reliable use under critical conditions. This work investigates and implements signal processing techniques designed to evaluate and visualize distortions in ranging signals with a low signal-to-noise ratio (SNR), focusing on the processing of short batches of signal samples. In particular, a novel approach is introduced in this thesis to overcome the limitations of the existing methodologies. Existing approaches often require high sampling rates, exceeding tens of mega-samples per second, for improved temporal resolution. They also typically need continuous tracking of the signal’s Doppler shift, carrier phase, and direct sequence spread spectrum (DSSS) code delay for durations longer than tens of seconds using dedicated tracking loops. Currently, analyzing GNSS signals in space is only possible through specific missions that capture signal samples. The first planned mission for this purpose is the Lunar GNSS Receiver Experiment (LuGRE). Its aim is to demonstrate GNSS-based positioning, navigation, and timing in lunar transfer orbits and on the lunar surface. The GNSS receiver will transmit observables like pseudorange, carrier phase, and Doppler measurements, as well as short signal recordings, to the ground without actively aiding the lunar lander’s navigation. This thesis, therefore, proposes a new approach that addresses these limitations, aiming to effectively visualize and analyze distortions in short signal recordings with low SNR and affected by Doppler shift. The proposed process starts with a high-sensitivity acquisition stage that leverages the knowledge of the DSSS code to assess the Doppler shift, Doppler rate, and data bits. An averaging process then reduces noise, leveraging the periodic nature of the ranging signal, which allows for subsequent chip shape analysis without a tracking loop, using only tens to hundreds of milliseconds of recorded signal data. Additionally, this work proposes a method to evaluate chip shape distortion from the averaging process, focusing on multipath assessment. This involves a goodness of fit test comparing the obtained chip shape to an unaffected one. A dataset of multipath-affected GNSS signals is generated starting from real signals, and the processing technique is then applied to evaluate performance, yielding promising results. Subsequently, this technique is integrated into the GNSS MATLAB receiver software developed by the Navigation Signal Analysis and Simulation (NavSAS) group at Politecnico di Torino. The study’s findings are expected to enhance the characterization of GNSS signal availability in the cislunar environment, particularly for lunar exploration.