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Research on constrained localization of ultrasound geometric distribution based on FHN neurons
The autopilot positioning process is mainly affected by three aspects: the first is the spatial geometric distribution of positioning sensors; the second is the screening of spurious observations; and the third is the equivalent ranging error. A constrained positioning method based on the geometric...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450310/ https://www.ncbi.nlm.nih.gov/pubmed/37248613 http://dx.doi.org/10.1177/00368504231168530 |
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author | Li, Weiwei Yuan, Sen Xiaorong, Zhou Qin, Long Xi, Yue |
author_facet | Li, Weiwei Yuan, Sen Xiaorong, Zhou Qin, Long Xi, Yue |
author_sort | Li, Weiwei |
collection | PubMed |
description | The autopilot positioning process is mainly affected by three aspects: the first is the spatial geometric distribution of positioning sensors; the second is the screening of spurious observations; and the third is the equivalent ranging error. A constrained positioning method based on the geometric distribution of FitzHugh–Nagumo (FHN) neurons is proposed. To reduce the geometric accuracy factor, a Horizontal Dilution Of Precision value algorithm with a weight factor was proposed by considering the spatial geometric distribution of base stations and the geometric relationship of anchor points. This paper proposes a geometric constraint data processing method for the error of the pseudo-observation value. Finally, considering the significant weak signal perception ability of the biological nervous system, and the stochastic resonance phenomenon caused by noise can enhance the ability of the neuronal system to detect weak signals, an ultrasonic receiving method based on the stochastic resonance characteristics of FHN neuronal system is proposed, to enhance the signal and reduce noise. The results show that under the optimized base station layout and data geometric constraint processing, the ultrasonic wave based on FHN neuron improves the accuracy of spurious observations, reduces the calculation amount of geometric constraint processing, and reduces the positioning error by 66.67%, which provides a new direction for improving the positioning accuracy. |
format | Online Article Text |
id | pubmed-10450310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104503102023-08-26 Research on constrained localization of ultrasound geometric distribution based on FHN neurons Li, Weiwei Yuan, Sen Xiaorong, Zhou Qin, Long Xi, Yue Sci Prog Original Manuscript The autopilot positioning process is mainly affected by three aspects: the first is the spatial geometric distribution of positioning sensors; the second is the screening of spurious observations; and the third is the equivalent ranging error. A constrained positioning method based on the geometric distribution of FitzHugh–Nagumo (FHN) neurons is proposed. To reduce the geometric accuracy factor, a Horizontal Dilution Of Precision value algorithm with a weight factor was proposed by considering the spatial geometric distribution of base stations and the geometric relationship of anchor points. This paper proposes a geometric constraint data processing method for the error of the pseudo-observation value. Finally, considering the significant weak signal perception ability of the biological nervous system, and the stochastic resonance phenomenon caused by noise can enhance the ability of the neuronal system to detect weak signals, an ultrasonic receiving method based on the stochastic resonance characteristics of FHN neuronal system is proposed, to enhance the signal and reduce noise. The results show that under the optimized base station layout and data geometric constraint processing, the ultrasonic wave based on FHN neuron improves the accuracy of spurious observations, reduces the calculation amount of geometric constraint processing, and reduces the positioning error by 66.67%, which provides a new direction for improving the positioning accuracy. SAGE Publications 2023-05-29 /pmc/articles/PMC10450310/ /pubmed/37248613 http://dx.doi.org/10.1177/00368504231168530 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Manuscript Li, Weiwei Yuan, Sen Xiaorong, Zhou Qin, Long Xi, Yue Research on constrained localization of ultrasound geometric distribution based on FHN neurons |
title | Research on constrained localization of ultrasound geometric distribution
based on FHN neurons |
title_full | Research on constrained localization of ultrasound geometric distribution
based on FHN neurons |
title_fullStr | Research on constrained localization of ultrasound geometric distribution
based on FHN neurons |
title_full_unstemmed | Research on constrained localization of ultrasound geometric distribution
based on FHN neurons |
title_short | Research on constrained localization of ultrasound geometric distribution
based on FHN neurons |
title_sort | research on constrained localization of ultrasound geometric distribution
based on fhn neurons |
topic | Original Manuscript |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450310/ https://www.ncbi.nlm.nih.gov/pubmed/37248613 http://dx.doi.org/10.1177/00368504231168530 |
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