Evidence map›Paper›PMID 39943271›Full record

ArticleSensors (Basel, Switzerland)2025

An Effective Single-Station Cooperative Node Localization Technique Using Multipath Spatiotemporal Information.

Di Bai, Xinran Li, Lingyun Zhou, Chunyong Yang, Yongqiang Cui, Liyun Bai, Yunhao Chen

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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.

Di BaiCollege of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Xinran LiCollege of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Lingyun ZhouCollege of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Chunyong YangCollege of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Yongqiang CuiCollege of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Liyun BaiWuhan Shipboard Communication Institute, Wuhan 430079, China.
Yunhao ChenYunnan Key Laboratory of Unmanned Autonomous Systems, Kunming 650500, China.ORCID 0000-0001-7471-3271

Funding

National Nature Science Foundation of China; Innovative Research Group Project of Natural Science Foundation of Hubei Province; the Foundation of Yunnan Key Laboratory of Unmanned Autonomous Systems 62201621,62401499; 2024AFA030; 202408YB09
6 · The paper itself

Abstract

Precise cooperative node localization is essential for the application of multifunctional integrated radio frequency (RF) sensor networks in military and civilian domains. Most geometric localization methods commonly rely on observation data from multiple receiving nodes or anchor points with known positions and synchronized clocks, producing complex system architectures and high construction costs. To address this, our paper proposes an effective single-station cooperative node localization technique, where the observation station only requires two antennas for operation. Leveraging prior knowledge of the geometry of surrounding structures, multiple virtual stations (VSs) are constructed by mining the spatiotemporal information contained in the multipath components (MPCs) to realize target positioning. The proposed method consists of two steps. In the first step, an unambiguous dual-antenna direction-finding algorithm is designed to extract the spatial information of MPCs and construct VSs, allowing a preliminary estimate of the source position (SP). In the second step, the path delays are extracted via matched filtering, while the spatiotemporal information is correlated based on the energy distribution for a more precise SP estimation. Simulations and experimental results demonstrate that our algorithm achieves high-precision single-station localization for a collaborative node, with positioning accuracy typically within 0.1 m.

Indexed as

collaborative sensor nodedual-antennamultipath spatiotemporal informationsingle-station localization

Identifiers

PMID39943271
PMCPMC11820139

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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.