Multipath propagation fundamentally limits wireless positioning in dense urban environments, yet existing mitigation methods often rely on specialized hardware or poorly interpretable data-driven models. We propose a multi-frequency cross-product area (MF-CPA) framework for multipath detection using only standard early-prompt-late I/Q correlator outputs, requiring no receiver modification. By analyzing correlator sensitivity to relative delay, phase, Doppler, and attenuation, we show that the six-branch I/Q structure enables a physically interpretable detection metric. Multi-frequency fusion further mitigates frequency-dependent blind zones inherent to single-frequency methods. Hardware-in-the-loop simulations in a 3D urban canyon demonstrate detection rates of 50–80% at low false-discovery levels across GPS, BDS, and Galileo signals. Real-world experiments confirm consistent spatial detection patterns, though validation remains qualitative due to the lack of ground-truth channel-state labels. The proposed framework provides a scalable and interpretable solution for robust multipath monitoring in next-generation wireless navigation systems.
Rong Yang, Yuquan Ma, Zhihong Li et al.· npj Wireless Technology· 0 citations
Real-Time Kinematic (RTK) and Network RTK (NRTK) techniques have been widely used to achieve centimeter-level high-precision positioning. The virtual reference station (VRS) concept is a practically efficient approach to obtain the benefits of using multiple reference stations. However, under this concept, the full covariance information of the virtual measurement errors is not provided to users, leading to suboptimal positioning performance. Toward RTK applications where communicational and computational resources are rich and accuracy performance is sensitive, this paper proposes an undifferenced, uncombined (UDUC) centralized RTK positioning framework that maximizes the benefits from using multiple reference stations. This approach produces an optimal positioning solution under Gaussian noise with correctly-specified error covariances. Monte Carlo simulations show that the performance gain of the proposed method increases with network scale. At a 50 km baseline, it reduces the vertical RMS error by 48% relative to the conventional approach. Additional simulations demonstrate stable performance under ionospheric covariance-model mismatch and irregular reference-station geometry. Real-data experiments covering compact networks, dynamic operation, multiple users, and longer baselines consistently confirm improved positioning accuracy, with vertical RMS reductions ranging from 23% to 90%. The experimental results also suggest that the proposed approach enables a faster ambiguity fix than conventional approaches. These results demonstrate the accuracy benefits of centralized multi-reference-station RTK processing.
