Multi-Type GNSS User Classification Using RANSAC-K-Means Clustering

The BeiDou Navigation Satellite System (BDS-3) has provided positioning, navigation and timing (PNT) services to global users across land, maritime, and aviation. However, how to classify these three types of users with complex movement patterns poses great challenges to the work of monitoring and evaluating the PNT system. To accurately classify multi-type global navigation satellite system (GNSS) users, this paper proposes a method that combines random sample consensus (RANSAC) and K-means clustering to track the movements of massive users and classify them based on their dynamic characteristics in different areas, which is noted as RANSAC-K-means. The simulated massive user data show that the recognition rate of the proposed algorithm exceeds 83.22%, and compared with the conventional method, the proposed RANSAC-K-means method improved the recognition rate by 11.16%. The RANSAC-K-means method can provide more accurate clustering results under the situations where multi-type users present dynamic characteristics with significant differences, showing significant stability and robustness. The proposed method is more suitable for monitoring and evaluating the service performance of satellite navigation systems.