Evidence map›Paper›PMID 41094850›Full record

ArticleSensors (Basel, Switzerland)2025

Eliminate Dynamic Error of A-PNAS High-Precision Time Synchronization Using Multi-Sensor Combination.

Zhenling Wang, Haihong Tao, Fang Hao, Yilong Liu, Zhengyong Wang

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. 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.

  1. Article
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

5 authors.

Zhenling WangNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Haihong TaoNational Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Fang HaoThe 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
Yilong LiuThe 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.
Zhengyong WangThe 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China.

Funding

Research Fund of The 54th Research Institute of China Electronics Technology Group Corporation. none
6 · The paper itself

Abstract

High-precision time synchronization among nodes of the airborne-based pseudolite navigation augmentation positioning system (A-PNAS) is a crucial indicator for ensuring the accuracy of positioning services. Due to the flight characteristics and external factors' influence, the airborne platform usually undergoes random motion. Therefore, the time-varying effect errors and Doppler effect errors will be introduced into the clock skew measurement results during the time-synchronous processing. In A-PNAS with meter-level positioning accuracy, the time synchronization accuracy (TSA) between nodes usually needs to be within 2 ns. These dynamic errors will have an impact on the TSA between nodes, which cannot be ignored. Based on the analysis of the principle of dynamic error generation and the available sensors, a multi-sensor combination method for correcting dynamic errors is proposed. This method calculates and corrects the dynamic errors based on the motion measurements from sensors. The simulation test results show that the degree of improvement in correcting dynamic errors by this method is basically close to 80%. It can effectively meet the requirements of high-precision time synchronization system and can provide an effective reference for the high-precision time synchronization processing of similar space-based platform collaborative systems.

Indexed as

A-PNASclock skewdynamic errormulti-sensor combinationtime synchronization

Identifiers

PMID41094850
PMCPMC12526708

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.