Indoor Human Localization Using Electrostatic Sensors and Compressive Sensing Techniques
Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activ...
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| Published in | IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 10 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of <inline-formula> <tex-math notation="LaTeX">4.2\times 4.2 </tex-math></inline-formula> m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes. |
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| AbstractList | Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of [Formula Omitted] m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes. Indoor human localization is of great significance in a variety of applications, including navigation, healthcare, security, and many other location-based services. This article presents a passive indoor localization method that exploits the varying electric fields naturally generated by human activities. An array of electrostatic sensors capable of passive, long-range sensing is developed using charge amplifiers. Human localization is formulated as an inverse problem that aims to reconstruct the charge distribution within the target area from sensor measurements. The spatial sensitivity matrix is preprocessed using QR factorization, and then, compressive sensing is used to find the sparse solution. Experiments were conducted in an office environment of <inline-formula> <tex-math notation="LaTeX">4.2\times 4.2 </tex-math></inline-formula> m. Results obtained show that the localization accuracy is location-dependent and a median error less than 0.26 m has been achieved. Although the sensor signals are vulnerable to a variety of factors, the localization method exhibits strong robustness against environmental and subject changes. |
| Author | Hu, Yonghui Wang, Junkai Yan, Yong Li, Yi |
| Author_xml | – sequence: 1 givenname: Yonghui orcidid: 0000-0002-7228-5418 surname: Hu fullname: Hu, Yonghui email: huyhui@gmail.com organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 2 givenname: Yi orcidid: 0009-0006-8689-6451 surname: Li fullname: Li, Yi organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 3 givenname: Junkai orcidid: 0009-0005-6111-5551 surname: Wang fullname: Wang, Junkai organization: School of Control and Computer Engineering, North China Electric Power University, Beijing, China – sequence: 4 givenname: Yong orcidid: 0000-0001-7135-5456 surname: Yan fullname: Yan, Yong organization: Hangzhou International Innovation Institute, Beihang University, Hangzhou, China |
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| SubjectTerms | Accuracy Charge amplifier Charge distribution compressive sensing Electric fields Electric potential Electrodes electrostatic sensor Electrostatics Floors indoor human localization Inverse problems Localization method Location awareness Location based services QR factorization Sensor arrays Sensors Temperature sensors |
| Title | Indoor Human Localization Using Electrostatic Sensors and Compressive Sensing Techniques |
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