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Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence
Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741196/ https://www.ncbi.nlm.nih.gov/pubmed/36502148 http://dx.doi.org/10.3390/s22239450 |
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author | Chen, Xuan-Ying Wen, Chih-Yu Sethares, William A. |
author_facet | Chen, Xuan-Ying Wen, Chih-Yu Sethares, William A. |
author_sort | Chen, Xuan-Ying |
collection | PubMed |
description | Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject’s motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target’s motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework. |
format | Online Article Text |
id | pubmed-9741196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97411962022-12-11 Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence Chen, Xuan-Ying Wen, Chih-Yu Sethares, William A. Sensors (Basel) Article Pyroelectric infrared (PIR) sensors are low-cost, low-power, and highly reliable sensors that have been widely used in smart environments. Indoor localization systems may be wearable or non-wearable, where the latter are also known as device-free localization systems. Since binary PIR sensors detect only the presence of a subject’s motion in their field of view (FOV) without other information about the actual location, information from overlapping FOVs of multiple sensors can be useful for localization. This study introduces the PIRILS (pyroelectric infrared indoor localization system), in which the sensing signal processing algorithms are augmented by deep learning algorithms that are designed based on the operational characteristics of the PIR sensor. Expanding to the detection of multiple targets, the PIRILS develops a quantized scheme that exploits the behavior of an artificial neural network (ANN) model to demonstrate localization performance in tracking multiple targets. To further improve the localization performance, the PIRILS incorporates a data augmentation strategy that enhances the training data diversity of the target’s motion. Experimental results indicate system stability, improved positioning accuracy, and expanded applicability, thus providing an improved indoor multi-target localization framework. MDPI 2022-12-02 /pmc/articles/PMC9741196/ /pubmed/36502148 http://dx.doi.org/10.3390/s22239450 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Xuan-Ying Wen, Chih-Yu Sethares, William A. Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title | Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title_full | Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title_fullStr | Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title_full_unstemmed | Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title_short | Multi-Target PIR Indoor Localization and Tracking System with Artificial Intelligence |
title_sort | multi-target pir indoor localization and tracking system with artificial intelligence |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741196/ https://www.ncbi.nlm.nih.gov/pubmed/36502148 http://dx.doi.org/10.3390/s22239450 |
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