What does this research mean for the field?

The generalized Matérn process (GMP) provides a unified and more flexible noise model family for GNSS coordinate series that consistently outperforms existing models in optimal model selection. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop a unified generalized Matérn process noise model that bridges generalized Gauss–Markov and Matérn process models for GNSS coordinate series noise characterization.

September 3, 2026Remote SensingOpen Access

Generalized Matérn Process for GNSS Coordinate Series Noise Modeling

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Authors

YHYueyang Huan GCGuobin Chang LPLei PengChina-Japan Friendship Hospital

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Implication

Methodological study demonstrates a unified generalized Matérn process for GNSS noise modeling across global stations, suggesting improved stochastic flexibility and signal estimation.

Key Points

To develop a unified generalized Matérn process noise model that bridges generalized Gauss–Markov and Matérn process models for GNSS coordinate series noise characterization.
Formulated the generalized Matérn process (GMP) framework by embedding a fractional step size hyperparameter μ into the differencing operator, linking generalized Gauss–Markov (μ = 1) and Matérn processes (μ → 0).
Assessed the preferred parameter range of μ across 420 GNSS coordinate series from 140 global stations accounting for region, coordinate component, and series length.
Conducted large-scale model performance validation across 846 GNSS coordinate series from 282 global stations using AIC, BIC, and BICtp selection criteria.
Preferred values for the fractional hyperparameter μ robustly concentrated within the interval [0.7, 1] across diverse geographic regions, observation lengths, and coordinate components.
Models combining white noise with the generalized Matérn process consistently accounted for a predominant share of optimal models under AIC, BIC, and BICtp evaluations.

Cite This Study

Huan et al. (2026) studied this question.

synapsesocial.com/papers/6a993586636c6408cfa7dc83 https://doi.org/10.3390/rs18172932

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Implication

Methodological study demonstrates a unified generalized Matérn process for GNSS noise modeling across global stations, suggesting improved stochastic flexibility and signal estimation.

Generalized Matérn Process for GNSS Coordinate Series Noise Modeling

Discussion

Member takes

Implication

Key Points

Cite This Study

Generalized Matérn Process for GNSS Coordinate Series Noise Modeling

Discussion

Member takes

Implication

Key Points

Cite This Study

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