High-Precision BDS PPP Positioning Method Based on SSR Correction Prediction

The interruption of real-time state space representation (SSR) corrections significantly degrades the performance of precise point positioning (PPP). To address this challenge, we propose a novel residual-enhanced iTransformer model specifically designed for BeiDou navigation satellite system (BDS) SSR prediction. Unlike conventional approaches including polynomial fitting, harmonic modeling, and autoregressive moving average (ARMA) methods, our framework innovatively integrates residual networks with the iTransformer architecture to effectively capture the complex nonlinear characteristics and non-stationary patterns in satellite clock offsets. The model demonstrates remarkable performance improvements, achieving 72–85% reduction in prediction error compared with traditional ARMA models. Experimental results show that, within 2 h prediction windows, orbit corrections achieve better than 0.1 m (radial), 0.2 m (along-track), and 0.2 m (cross-track) accuracy, while clock corrections maintain sub-0.5 ns precision. Most importantly, during 30 min SSR outages, BDS real-time PPP utilizing our predicted corrections sustains positioning accuracy within 10 cm in all east, north, and up directions, representing over 80% improvement compared with traditional time-differenced carrier phase (TDCP) methods. This work establishes an effective solution for maintaining high-precision positioning services during SSR interruptions.