Comprehensive Analysis of Beidou-3 PPP-B2b Performance Based on Adaptive Robust Extend Kalman Filter

Beidou-3 navigation satellite system (BDS-3) initiated a real-time service for precise point positioning (PPP) using the B2b signal, mainly for users in China and surrounding areas. In this paper, the performance of PPP-B2b service is experimentally analyzed first. Then, the ionosphere-free model is established. In order to solve the problem of slow convergence for traditional PPP, an adaptive robust extend Kalman filter (AREKF) algorithm is developed. Unlike the error compensation models, it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector. The experimental results are analyzed last. Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating. Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes. Around 20% improvement in accuracy and 25% improvement in convergence speed are achieved, making it valuable for PPP processing.