GNSS positioning and interference Open access Peer reviewed

GNSS/eLoran Fusion-Based RAIM for Satellite-Deficient Environments

J. Q. Li, Huabing Wu

Sensors | Jul 6, 2026

Abstract

Abstract

Global Navigation Satellite Systems (GNSS) provide essential positioning, navigation, and timing (PNT) services for a wide range of safety-critical applications. However, GNSS performance degrades significantly in satellite-deficient or interference-prone environments. To address this limitation, this study proposes a hybrid GNSS/eLoran integrity monitoring framework based on a simplified Receiver Autonomous Integrity Monitoring (RAIM) architecture. In the proposed method, GNSS observations from satellite constellations and range-equivalent measurements from the enhanced Loran (eLoran) terrestrial system are jointly processed using a weighted least-squares estimator. Integrity monitoring is performed through a global chi-square consistency test combined with a solution separation strategy for fault identification and exclusion. Horizontal Protection Level (HPL) is derived from the covariance of the estimation process to ensure bounded positioning error under nominal and fault conditions. Unlike conventional GNSS-only RAIM, the proposed framework enables improved redundancy and fault observability in satellite-deficient scenarios by incorporating heterogeneous terrestrial measurements. Simulation experiments consider satellite faults, eLoran measurement disturbances, and inter-system clock bias effects. Results demonstrate that the proposed method maintains reliable fault detection capability and ensures that positioning errors remain consistently bounded by the protection level under all tested scenarios.

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Authors

Researchers on this paper

J. Q. Li

first | Chinese Academy of Sciences | ORCID 0009-0009-3914-1437

Huabing Wu

last | Chinese Academy of Sciences | ORCID 0000-0002-0936-6311

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Citation

BibTeX

@article{Li2026GNSS,
  title = {GNSS/eLoran Fusion-Based RAIM for Satellite-Deficient Environments},
  author = {J. Q. Li and Huabing Wu},
  journal = {Sensors},
  year = {2026},
  doi = {10.3390/s26134295},
  url = {https://doi.org/10.3390/s26134295}
}

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