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<p>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.</p>

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Scalable multipath detection from standard GNSS correlators using multifrequency diversity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Rong Yang,
  • Yuquan Ma,
  • Zhihong Li,
  • Miao Wang,
  • Li-Ta Hsu,
  • Xingqun Zhan

摘要

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.