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<p>Ambiguity resolution (AR) is fundamental to achieving high-precision real-time kinematic (RTK) positioning, yet its reliability remains compromised when conventional AR validation procedures fail. To address this limitation, this study proposes a robust ambiguity resolution framework that integrates partial ambiguity resolution (PAR) and best integer equivariant estimation (BIE), supported by a novel modified carrier-phase-based receiver autonomous integrity monitoring (CRAIM) strategy. The framework operates through a hierarchical three-tier fallback mechanism: Tier 1 employs PAR with modified CRAIM-based fault detection and exclusion (PAR-FDE) to deliver fixed solutions after conventional PAR failure; upon validation failure, Tier 2 activates BIE with modified CRAIM-based fault detection (BIE-FD) to provide robust alternatives; Tier 3 reverts to conventional BIE solutions only when both preceding stages are unsuccessful. The PAR-FDE component identifies and excludes incorrect integer ambiguities, whereas the BIE-FD component detects and isolates erroneous ambiguity candidates. Experimental validation using two distinct datasets demonstrates the framework’s superior performance in fault detection and exclusion during ambiguity resolution, concomitantly enhancing positioning accuracy, solution continuity, and availability. For Dataset No.1, the positioning accuracy of the framework achieves improvements of 89.08%, 92.60%, and 93.35% in the east, north, and up components, respectively, compared with the conventional PAR and BIE method (Mode #1). Additionally, the framework sustains prolonged continuous tracking epochs with three-dimensional (3D) position errorsconstrained within predefined error budgets, while attaining 98.25% horizontal availability at the 0.02&#xa0;m error threshold. Dataset No.2 results confirm a 0.5651&#xa0;m accuracy improvement in 3D positioning over conventional approaches. These findings substantiate the framework’s efficacy for safety–critical RTK applications requiring stringent performance guarantees.</p>

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A robust ambiguity resolution framework integrating PAR and BIE with modified CRAIM strategies

  • 期刊论文

  • 发表日期：

    2026年7月27日

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  • Xuezhen Li,
  • Hua Chen,
  • Zhetao Zhang,
  • Zelang Miao,
  • Zhao Li,
  • Weiping Jiang

摘要

Ambiguity resolution (AR) is fundamental to achieving high-precision real-time kinematic (RTK) positioning, yet its reliability remains compromised when conventional AR validation procedures fail. To address this limitation, this study proposes a robust ambiguity resolution framework that integrates partial ambiguity resolution (PAR) and best integer equivariant estimation (BIE), supported by a novel modified carrier-phase-based receiver autonomous integrity monitoring (CRAIM) strategy. The framework operates through a hierarchical three-tier fallback mechanism: Tier 1 employs PAR with modified CRAIM-based fault detection and exclusion (PAR-FDE) to deliver fixed solutions after conventional PAR failure; upon validation failure, Tier 2 activates BIE with modified CRAIM-based fault detection (BIE-FD) to provide robust alternatives; Tier 3 reverts to conventional BIE solutions only when both preceding stages are unsuccessful. The PAR-FDE component identifies and excludes incorrect integer ambiguities, whereas the BIE-FD component detects and isolates erroneous ambiguity candidates. Experimental validation using two distinct datasets demonstrates the framework’s superior performance in fault detection and exclusion during ambiguity resolution, concomitantly enhancing positioning accuracy, solution continuity, and availability. For Dataset No.1, the positioning accuracy of the framework achieves improvements of 89.08%, 92.60%, and 93.35% in the east, north, and up components, respectively, compared with the conventional PAR and BIE method (Mode #1). Additionally, the framework sustains prolonged continuous tracking epochs with three-dimensional (3D) position errorsconstrained within predefined error budgets, while attaining 98.25% horizontal availability at the 0.02 m error threshold. Dataset No.2 results confirm a 0.5651 m accuracy improvement in 3D positioning over conventional approaches. These findings substantiate the framework’s efficacy for safety–critical RTK applications requiring stringent performance guarantees.