Evidence map›Paper›PMID 35898008›Full record

ArticleSensors (Basel, Switzerland)2022

A Low-Cost and Efficient Indoor Fusion Localization Method.

Suqing Yan, Chunping Wu, Honggao Deng, Xiaonan Luo, Yuanfa Ji, Jianming Xiao

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

6 authors.

Suqing YanGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0000-0003-1189-6159
Chunping WuSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Honggao DengGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.
Xiaonan LuoGuangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China.
Yuanfa JiGuangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology, Guilin 541004, China.ORCID 0000-0001-8092-6679
Jianming XiaoDepartment of Science and Engineering, Guilin University, Guilin 541004, China.

Funding

Guang Xi Graduate Education Research on multimodal fusion navigation algorithmsGuangxi science and Technology Project Grant AD18216004, Grant AA18118039, and Grant AA19182007, Grant AA19254029, Grant AA20302022, and Grant AB21196041Guilin Science and Technology Project Grant 20210222-1National Natural Science Foundation of China Grant 61772149, Grant 61936002, and Grant 2018AAA0100300, Grant 6202780103, Grant 62033001, Grant 61861008, Grant 62061010, Grant 62161007
6 · The paper itself

Abstract

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Indexed as

acoustic signalimproved pedestrian dead reckonindoor localizationstep length estimation

Identifiers

PMID35898008
PMCPMC9371102

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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.