Enhancing ARAIM algorithm with Dynamic Satellite Ranking: An Effective Approach to Optimizing Computational Efficiency and Availability

Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing services worldwide, evolving from military origins to critical civilian infrastructure. GNSS consists of satellite constellations orbiting Earth at altitudes ranging from approximately 19,100 to 23,200 kilometers, depending on the specific constellation, broadcasting timed signals that ground-based receivers use to calculate their position through trilateration. The current GNSS landscape includes the U.S. GPS, Russia's GLONASS, the EU's Galileo, and China's BeiDou, along with regional systems like Japan's QZSS and India's NavIC. This multi-constellation environment enhances coverage, accuracy, and resilience. With more satellites visible, users can achieve reliable positioning even in obstructed environments and sub-meter accuracy under favorable conditions. Redundancy across constellations ensures availability, vital for safety-critical applications, while promoting global interoperability and reducing dependence on any single system. GNSS expansion enables high-integrity applications in aviation and autonomous vehicles, where precise positioning is crucial. However, this growth introduces challenges, including complex signal environments requiring advanced algorithms to process multi-source data. The integrity and reliability of GNSS for safety-critical applications are essential, prompting the development of techniques like Advanced Receiver Autonomous Integrity Monitoring (ARAIM) to leverage multi-constellation benefits while ensuring high integrity levels for critical operations. Considering these complexities and opportunities is essential for improving ARAIM performance, particularly through methods like Dynamic Satellite Ranking (DSR) in multi-constellation environments.