Evidence map›Paper›PMID 42655470›Full record

ArticleSensors (Basel, Switzerland)2026

Fisher-Information-Based Cooperative Sensor Node Pre-Selection for UWB-Aided GNSS-Denied UAV Swarm Localization Under Heterogeneous Ranging Noise.

Yanming Sun, Xiaoyan Du, Pihong Gong

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. 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

3 authors.

Yanming SunSchool of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.ORCID 0009-0003-8058-0630
Xiaoyan DuSchool of Transportation, Shandong University of Science and Technology, Qingdao 266590, China.
Pihong GongInternational Cooperation Center, National Development and Reform Commission, Beijing 100038, China.

Funding

Natural Science Foundation of Shandong Province ZR2021MG021Youth Innovation Technology Project of Higher School in Shandong Province 2021RW030
6 · The paper itself

Abstract

Ultra-wideband (UWB) inter-node ranging provides relative-distance constraints for cooperative localization in GNSS-denied UAV swarms, but dense candidate networks can exceed the available ranging slots, communication bandwidth, computation, and energy. This paper proposes a Fisher-information-based cooperative sensor node pre-selection method under heterogeneous ranging noise. All mobile nodes remain in the localization state, while the selected nodes induce the active ranging-link set. Selected-node, induced-link, ranging-slot, and normalized general-resource budgets are represented separately. Using predicted geometry and estimated link-quality weights, a gauge-free normalized Fisher information matrix combines link geometry, link-quality-dependent weights, and topology-induced coupling. A trace-based generalized GDOP (G-GDOP) criterion is optimized by a two-stage greedy heuristic with recursive matrix updates. The experiments show that G-GDOP is a local observability and information-quality metric rather than a direct predictor of topology-level nonlinear recovery error. Within the same topology, normalized local RMSE increased from 0.698 [0.673, 0.752] in the Low G-GDOP group to 1.014 [0.999, 1.068] and 1.980 [1.806, 2.180] in the Medium and High groups. Increasing the selected-node budget from

Indexed as

cooperative sensor node pre-selectionFisher information matrixgeneralized GDOPGNSS-denied navigationheterogeneous ranging noiseresource-constrained sensingUWB cooperative localization

Identifiers

PMID42655470
PMCPMC13517213

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.