The ionosphere has been the most challenging source of error to mitigate within the community of global navigation satellite system (GNSS)-based safety-critical systems. Users of those systems should be assured that the difference between an unknown true position and a system-derived position estimate is bounded with an extremely high degree of confidence. One of the major concerns for meeting this requirement, known as integrity, is ionosphere-induced error or discontinuity of GNSS signals significant enough to threaten the safety of users. The potentially hazardous ionospheric anomalies of interest in this article are ionospheric spatial decorrelation and ionospheric scintillation under disturbed conditions. As the demand of safety-critical navigation applications increases with the rapid growth of the autonomous vehicle sector, ionospheric monitoring and mitigation techniques become more important to support such systems.

Ground-based Augmentation System (GBAS) augments Global Navigation Satellite Systems (GNSS) to support the precision approach and landing of aircraft. To guarantee integrity, existing single-frequency GBAS utilizes position-domain geometry screening to eliminate potentially unsafe satellite geometries by inflating one or more broadcast GBAS parameters. However, GBAS availability can be drastically impacted in low-latitude regions where severe ionospheric conditions have been observed. Thus, we developed a novel geometry-screening algorithm in this study to improve GBAS availability in low-latitude regions. Simulations demonstrate that the proposed method can provide 5-8 percentage point availability enhancement of GBAS at Galeão airport near Rio de Janeiro, Brazil, compared to existing methods.

Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.

Deep signal fading due to ionospheric scintillation severely impacts global navigation satellite system (GNSS)‐based applications. GNSS receivers run the risk of signal loss under deep fading, which directly leads to a significant decrease in navigation availability. The impact of scintillation on GNSS‐based applications can be mitigated via dual‐frequency signals which provide a backup channel. However, the benefit of dual‐frequency diversity highly depends on the correlation of fading processes between signals at different frequencies. This paper proposes a Markov chain‐based model that simulates the actual behavior of correlated fading processes in dual‐frequency channels. A set of recorded scintillation data was used to capture transitions among all fading states based on the fading and recovery of each signal frequency. A statistical study of deep fading characteristics in this data revealed that the Markov chain‐based model accurately generates realistic correlated fading processes. Using the proposed model, aviation availability of localizer performance with vertical guidance down to a 200‐foot decision height (“LPV‐200”) under a strong scintillation scenario is analyzed by considering the effects of signal outages due to deep fading. A parametric analysis of the availability resulting from variations in mean time to loss of lock, mean time to reacquisition, and ionospheric delay uncertainty was conducted to investigate the performance standards on GNSS‐based aviation under scintillation. The analysis results demonstrate a significant benefit of frequency diversity on aviation availability during scintillation. This model will further enable the assessment of GNSS‐based availability for aviation and other applications under a full range of scintillation conditions.

Global Navigation Satellite Systems (GNSS) are now recognized as cost-effective tools for ionospheric studies by providing the global coverage through worldwide networks of GNSS stations. While GNSS networks continue to expand to improve the observability of the ionosphere, the amount of poor quality GNSS observation data is also increasing and the use of poor-quality GNSS data degrades the accuracy of ionospheric measurements. This paper develops a comprehensive method to determine the quality of GNSS observations for the purpose of ionospheric studies. The algorithms are designed especially to compute key GNSS data quality parameters which affect the quality of ionospheric product. The quality of data collected from the Continuously Operating Reference Stations (CORS) network in the conterminous United States (CONUS) is analyzed. The resulting quality varies widely, depending on each station and the data quality of individual stations persists for an extended time period. When compared to conventional methods, the quality parameters obtained from the proposed method have a stronger correlation with the quality of ionospheric data. The results suggest that a set of data quality parameters when used in combination can effectively select stations with high-quality GNSS data and improve the performance of ionospheric data analysis.