Evidence map›Paper›PMID 42590711›Full record

ReviewSensors (Basel, Switzerland)2026

A Tutorial Review of Statistical Snapshot Detectors for GNSS/RAIM Fault Detection: Unified Derivations and Detector Relationships.

Penggao Yan, Baoshan Song, Yuan Li, Li-Ta Hsu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

4 authors.

Penggao YanDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0003-0994-9258
Baoshan SongDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0003-2967-0350
Yuan LiDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
Li-Ta HsuDepartment of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China.ORCID 0000-0002-0352-741X

Funding

The Research Grants Council of the Hong Kong Special Administrative Region, China C5032-23GF
6 · The paper itself

Abstract

The receiver autonomous integrity monitoring (RAIM) and Global Navigation Satellite System (GNSS) fault detection literature uses a group of statistical detectors that are often introduced with different names, coordinate systems, and derivation styles. This makes it difficult for new researchers to determine whether two methods use different information or only express the same inconsistency through different statistics. This paper provides a detector-centered tutorial review of statistical snapshot fault detection with a unified whitened linearized model. The chi-squared detector, parity-space detector, Baarda w-test, range comparison detector, jackknife detector, solution separation detector, and generalized likelihood-ratio test are derived with consistent notation. For each detector, the statistic construction, null and alternative distributions, threshold rule, and minimum detectable bias (MDB) are presented. A relationship map was developed to distinguish exact equivalence, projection relations and linear transformations among these detectors. An illustrative validation was then conducted with a real satellite geometry collected in an urban environment and synthetic Gaussian faults. The results verify the relationship checks, detection-probability behavior, and MDB calculations in a reproducible setting.

Indexed as

fault detectionGNSSlikelihood-ratio testparity spaceprojectionRAIMsolution separation

Identifiers

PMID42590711
PMCPMC13469922

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.