GVMD-NLM: A Hybrid Denoising Method for GNSS Buoy Elevation Time Series Using Optimized VMD and Non-Local Means Filtering.

Huanghuang Zhang, Shengping Wang, Chao Dong, Guangyu Xu, Xiaobo Cai

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Extraction of Time-Varying Signals in GNSS and Geophysical Interpretation: Methods, Advances, and Challenges.Sensors (Basel, Switzerland) · 2026
Review

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Huanghuang ZhangSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.

Shengping WangSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.

Chao DongKey Laboratory of Marine Environment Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China.

Guangyu XuSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.

Xiaobo CaiSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.

Funding

Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Open Fund Project MESTA-2023-A003National Natural Science Foundation of China 42174013National Natural Science Foundation of China 42266006Research Foundation of Education Bureau of Jiangxi Province GJJ2200704

6 · The paper itself

Abstract

GNSS buoys are essential for real-time elevation monitoring in coastal waterways, yet the vertical coordinate time series are frequently contaminated by complex non-stationary noise, and existing denoising methods often rely on empirical parameter settings that compromise reliability. This paper proposes GVMD-NLM, a hybrid denoising framework optimized by an improved Grey Wolf Optimizer (GWO). The method introduces an adaptive convergence factor decay function derived from the Sigmoid function to automatically determine the optimal parameters (

Indexed as

GNSS elevation time seriesgrey wolf optimizernon-local meanssignal denoisingvariational mode decomposition

Identifiers

PMID41600317

DOI10.3390/s26020522

PMCPMC12845906

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