Evidence map›Paper›PMID 42740213›Full record

ArticleSensors (Basel, Switzerland)2026

Tightly Coupled LEO SOP/INS Integrated Navigation Using an Adaptive Unscented Kalman Filter Framework.

Yuan Zhao, Jinghan Feng, Yu Jiang, Zhe Fan, Bing Xie, Chengkai Tang, Yangyang Liu

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

7 authors.

Yuan ZhaoSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
Jinghan FengSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.ORCID 0009-0005-2449-5225
Yu JiangSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
Zhe FanSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.
Bing XieDepartment of Network Security, Henan Police College, Zhengzhou 450046, China.
Chengkai TangSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.ORCID 0000-0002-9008-0061
Yangyang LiuSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.

Funding

Education Department of Shaanxi Provincial Government 24JK0574Key Technology Research and Development Program of Henan Province 252102210021
6 · The paper itself

Abstract

With the rapid development of low earth orbit (LEO) constellations, LEO signals of opportunity (LEO-SOP) navigation has attracted extensive attention for positioning services in complex-denied environments. However, LEO-SOP suffers from limited coverage multiplicity and time-varying observation noise, leading to degraded positioning accuracy and availability. To address this issue, this paper applies an innovation-based adaptive unscented Kalman filtering (AUKF) scheme to LEO/inertial navigation system (INS) tightly coupled integration. By constructing the innovation sequence, the impact mechanism of noise uncertainty on filtering performance is analyzed, and an online noise covariance estimation strategy is designed to achieve dynamic adaptive compensation for both process and measurement noise. Simulation results demonstrate that the proposed method effectively suppresses the destabilizing effects of LEO observation noise and significantly improves the robustness and estimation accuracy of the filter in complex environments.

Indexed as

adaptive unscented Kalman filterinertial navigation systemintegrated navigationLEO satellitessignals of opportunity

Identifiers

PMID42740213
PMCPMC13568177

What OpenQuestion holds

Textmetadata
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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.