GNSS is the global standard for positioning, navigation, and timing (PNT) services. However, achieving high-accuracy positioning in harsh environments remains an open challenge. Buildings, vegetation, and reflective surfaces obstruct GNSS signals, causing non-line-of-sight (NLoS) and multipath effects. These limitations severely impact the reliability of urban positioning, affecting the deployment of IoT systems for civilian applications and the transition to smart cities. This work focuses on identifying and integrating complementary technologies to improve the accuracy, reliability, and availability of PNT services. We propose a multi-layered approach based on the hybridization of GNSS with two emerging technologies: 5G, which is capable to provide advanced positioning services, and Low Earth Orbit Positioning, Navigation, and Timing (LEO-PNT) satellites, the next generation of constellations specifically designed for high-accuracy PNT. The 5G-related activities have been carried out within the HYPER-5G project, funded by the ESA NAVISP program and involving Politecnico di Milano together with external industrial partners. The key innovative aspect of this work lies in the use of real 5G measurements collected during a dedicated field campaign. At the project kick-off (2022), no studies in the literature had reported results based on real 5G data, relying instead exclusively on synthetic observations. For the first time, therefore, we present results obtained from the processing of real-world 5G measurements. Concerning LEO-PNT, this research has been carried out in collaboration with the European Space Agency, including a research period at ESA ESTEC (The Netherlands). As of 2025, no operational LEO-PNT constellations are available; therefore, the results presented here rely on the processing of synthetic data generated according to models available in the literature. For constellation design and modeling, ESA’s reference work has been adopted. A specific model has been developed to generate synthetic observations from a satellite to a known receiver, providing a realistic testbed for integration studies. We present the development of a software capable of jointly processing real GNSS measurements with field-collected 5G observations and synthetic LEO data by implementing a positioning engine based on an Extended Kalman Filter (EKF). In addition, the software enables preliminary investigations of the considered environment, allowing an a priori assessment of the expected solution quality through a geometric evaluations based on Dilution of Precision indexes (DOP). Different environmental conditions and processing methods are tested to highlight the improvements achievable from both technologies. Results confirm that this hybrid approach enhances positioning performance, significantly improving the overall accuracy of the solution, particularly in the context of Precise Point Positioning (PPP). We present the development of a software capable of jointly processing real GNSS measurements with field-collected 5G observations and synthetic LEO data by implementing a positioning engine based on an Extended Kalman Filter (EKF). The software also supports both Standard Point Positioning (SPP) and Precise Point Positioning (PPP), ensuring flexibility across different levels of accuracy. In addition, it enables preliminary investigations of the considered environment, allowing an assessment of the expected solution quality through a geometric evaluations based on Dilution of Precision indexes (DOP). Different environmental conditions and processing methods are tested to highlight the improvements achievable from both technologies. Results confirm that this hybrid approach enhances positioning performance, significantly improving the overall accuracy of the solution, particularly in the context of Precise Point Positioning (PPP).
Il GNSS è lo standard globale per i servizi di posizionamento, navigazione e sincronizzazione (PNT). Tuttavia, ottenere un posizionamento altamente accurato in ambienti difficili rimane una sfida aperta. Edifici, vegetazione e superfici riflettenti ostacolano i segnali GNSS, causando effetti di non linea di vista (NLoS) e multipath. Queste limitazioni incidono gravemente sull'affidabilità del posizionamento urbano, influenzando l'implementazione dei sistemi IoT per applicazioni civili e la transizione verso le città intelligenti. Questo lavoro si concentra sull'identificazione e l'integrazione di tecnologie complementari per migliorare la precisione, l'affidabilità e la disponibilità dei servizi PNT. Proponiamo un approccio multistrato basato sull'ibridazione del GNSS con due tecnologie emergenti: il 5G, in grado di fornire servizi di posizionamento avanzati, e i satelliti LEO-PNT (Low Earth Orbit Positioning, Navigation, and Timing), la prossima generazione di costellazioni progettate specificamente per il PNT ad alta precisione. Le attività relative al 5G sono state svolte nell'ambito del progetto HYPER-5G, finanziato dal programma NAVISP dell'ESA e che coinvolge il Politecnico di Milano insieme a partner industriali esterni. L'aspetto innovativo chiave di questo lavoro risiede nell'uso di misurazioni 5G reali raccolte durante una campagna sul campo dedicata. All'avvio del progetto (2022), nessuno studio in letteratura aveva riportato risultati basati su dati 5G reali, basandosi invece esclusivamente su osservazioni sintetiche. Per la prima volta, quindi, presentiamo i risultati ottenuti dall'elaborazione di misurazioni 5G reali. Per quanto riguarda LEO-PNT, questa ricerca è stata condotta in collaborazione con l'Agenzia Spaziale Europea, compreso un periodo di ricerca presso l'ESA ESTEC (Paesi Bassi). A partire dal 2025, non saranno disponibili costellazioni LEO-PNT operative; pertanto, i risultati qui presentati si basano sull'elaborazione di dati sintetici generati secondo modelli disponibili in letteratura. Per la progettazione e la modellizzazione della costellazione è stato adottato il lavoro di riferimento dell'ESA. È stato sviluppato un modello specifico per generare osservazioni sintetiche da un satellite a un ricevitore noto, fornendo un banco di prova realistico per gli studi di integrazione. Presentiamo lo sviluppo di un software in grado di elaborare congiuntamente misurazioni GNSS reali con osservazioni 5G raccolte sul campo e dati LEO sintetici, implementando un motore di posizionamento basato su un filtro di Kalman esteso (EKF). Inoltre, il software consente indagini preliminari dell'ambiente considerato, permettendo una valutazione a priori della qualità della soluzione prevista attraverso valutazioni geometriche basate su indici di diluizione della precisione (DOP). Vengono testate diverse condizioni ambientali e metodi di elaborazione per evidenziare i miglioramenti ottenibili da entrambe le tecnologie. I risultati confermano che questo approccio ibrido migliora le prestazioni di posizionamento, aumentando significativamente la precisione complessiva della soluzione, in particolare nel contesto del Precise Point Positioning (PPP). Presentiamo lo sviluppo di un software in grado di elaborare congiuntamente misurazioni GNSS reali con osservazioni 5G raccolte sul campo e dati LEO sintetici, implementando un motore di posizionamento basato su un filtro di Kalman esteso (EKF). Il software supporta anche il posizionamento puntuale standard (SPP) e il posizionamento puntuale preciso (PPP), garantendo flessibilità a diversi livelli di accuratezza. Inoltre, consente indagini preliminari dell'ambiente considerato, permettendo una valutazione della qualità prevista della soluzione attraverso valutazioni geometriche basate su indici di diluizione della precisione (DOP). Sono state testate diverse condizioni ambientali e metodi di elaborazione per evidenziare i miglioramenti ottenibili da entrambe le tecnologie. I risultati confermano che questo approccio ibrido migliora le prestazioni di posizionamento, aumentando significativamente la precisione complessiva della soluzione, in particolare nel contesto del posizionamento puntuale preciso (PPP).
Hybridization of GNSS with 5G and LEO-PNT for Positioning in harsh environment
ALGHISI, MARIANNA
2025/2026
Abstract
GNSS is the global standard for positioning, navigation, and timing (PNT) services. However, achieving high-accuracy positioning in harsh environments remains an open challenge. Buildings, vegetation, and reflective surfaces obstruct GNSS signals, causing non-line-of-sight (NLoS) and multipath effects. These limitations severely impact the reliability of urban positioning, affecting the deployment of IoT systems for civilian applications and the transition to smart cities. This work focuses on identifying and integrating complementary technologies to improve the accuracy, reliability, and availability of PNT services. We propose a multi-layered approach based on the hybridization of GNSS with two emerging technologies: 5G, which is capable to provide advanced positioning services, and Low Earth Orbit Positioning, Navigation, and Timing (LEO-PNT) satellites, the next generation of constellations specifically designed for high-accuracy PNT. The 5G-related activities have been carried out within the HYPER-5G project, funded by the ESA NAVISP program and involving Politecnico di Milano together with external industrial partners. The key innovative aspect of this work lies in the use of real 5G measurements collected during a dedicated field campaign. At the project kick-off (2022), no studies in the literature had reported results based on real 5G data, relying instead exclusively on synthetic observations. For the first time, therefore, we present results obtained from the processing of real-world 5G measurements. Concerning LEO-PNT, this research has been carried out in collaboration with the European Space Agency, including a research period at ESA ESTEC (The Netherlands). As of 2025, no operational LEO-PNT constellations are available; therefore, the results presented here rely on the processing of synthetic data generated according to models available in the literature. For constellation design and modeling, ESA’s reference work has been adopted. A specific model has been developed to generate synthetic observations from a satellite to a known receiver, providing a realistic testbed for integration studies. We present the development of a software capable of jointly processing real GNSS measurements with field-collected 5G observations and synthetic LEO data by implementing a positioning engine based on an Extended Kalman Filter (EKF). In addition, the software enables preliminary investigations of the considered environment, allowing an a priori assessment of the expected solution quality through a geometric evaluations based on Dilution of Precision indexes (DOP). Different environmental conditions and processing methods are tested to highlight the improvements achievable from both technologies. Results confirm that this hybrid approach enhances positioning performance, significantly improving the overall accuracy of the solution, particularly in the context of Precise Point Positioning (PPP). We present the development of a software capable of jointly processing real GNSS measurements with field-collected 5G observations and synthetic LEO data by implementing a positioning engine based on an Extended Kalman Filter (EKF). The software also supports both Standard Point Positioning (SPP) and Precise Point Positioning (PPP), ensuring flexibility across different levels of accuracy. In addition, it enables preliminary investigations of the considered environment, allowing an assessment of the expected solution quality through a geometric evaluations based on Dilution of Precision indexes (DOP). Different environmental conditions and processing methods are tested to highlight the improvements achievable from both technologies. Results confirm that this hybrid approach enhances positioning performance, significantly improving the overall accuracy of the solution, particularly in the context of Precise Point Positioning (PPP).| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/250836