Massive MIMO Beam ID-Based Positioning Method With Low Earth Orbit Satellite Mega Constellations

Abstract

The growth of satellite-based positioning methods has revolutionized global navigation by providing reliable geolocation capabilities. However, traditional Global Navigation Satellite Systems (GNSS) are increasingly vulnerable to threats like jamming, spoofing, and interception, undermining their reliability in critical applications such as in-flight navigation and emergency services. To address these challenges, Low Earth Orbit (LEO) satellite constellations have emerged as a promising complement to GNSS infrastructure. LEO satellites, orbiting at lower altitudes with higher density, offer improved signal availability, reduced degradation, and better reception on Earth. This paper presents a LEO satellite-based positioning method via massive multiple-input multiple-output (mMIMO) beamforming antennas. The proposed technique not only mitigates GNSS vulnerabilities but also introduces a passive sensing mechanism that facilitates positioning without complex timing synchronization, improving resilience in jamming-prone environments. By utilizing LEO satellite beam identifiers as geographic pointers, our method enables precise positioning through LEO satellite ephemeris and beam pattern data. We validate this beam-based method through simulations, LEO constellation data, vehicular drive-test datasets, and probabilistic positioning models. Positioning results from the first dataset show a mean absolute error (MAE) of 9.15 meters and a 95th percentile error (p95%) of 19.07 meters when combining LEO satellite data with inertial motion data from a moving vehicle. Meanwhile, GNSS accuracy was MAE = 26.6 meters and p95% = 56.6 meters. The second dataset showed consistent results with accuracy improvements in MAE from 18.55 to 9.42 meters, RMSE from 22.24 to 12.05 meters, and p95% from 36.38 to 21.18 meters, compared to GNSS. These findings highlight the potential of LEO satellite positioning to improve accuracy and reliability in challenging environments, with implications for critical applications such as remote sensing, emergency response, search and rescue, and situational awareness.