Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images
Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limita...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/4014 |
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| author | Juil Jeon Myungin Ji Jungho Lee Kyeong-Soo Han Youngsu Cho |
| author_facet | Juil Jeon Myungin Ji Jungho Lee Kyeong-Soo Han Youngsu Cho |
| author_sort | Juil Jeon |
| collection | DOAJ |
| description | Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. |
| format | Article |
| id | doaj-art-66a53c6101cb43fdb3a145ba34ecfca5 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-66a53c6101cb43fdb3a145ba34ecfca52025-08-20T02:14:23ZengMDPI AGRemote Sensing2072-42922024-10-011621401410.3390/rs16214014Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map ImagesJuil Jeon0Myungin Ji1Jungho Lee2Kyeong-Soo Han3Youngsu Cho4Air Mobility Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaAir Mobility Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaAir Mobility Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaAir Mobility Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaAir Mobility Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Republic of KoreaSmartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical.https://www.mdpi.com/2072-4292/16/21/4014matching-map imagepositioningdeep learning |
| spellingShingle | Juil Jeon Myungin Ji Jungho Lee Kyeong-Soo Han Youngsu Cho Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images Remote Sensing matching-map image positioning deep learning |
| title | Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images |
| title_full | Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images |
| title_fullStr | Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images |
| title_full_unstemmed | Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images |
| title_short | Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images |
| title_sort | deep learning based emergency rescue positioning technology using matching map images |
| topic | matching-map image positioning deep learning |
| url | https://www.mdpi.com/2072-4292/16/21/4014 |
| work_keys_str_mv | AT juiljeon deeplearningbasedemergencyrescuepositioningtechnologyusingmatchingmapimages AT myunginji deeplearningbasedemergencyrescuepositioningtechnologyusingmatchingmapimages AT jungholee deeplearningbasedemergencyrescuepositioningtechnologyusingmatchingmapimages AT kyeongsoohan deeplearningbasedemergencyrescuepositioningtechnologyusingmatchingmapimages AT youngsucho deeplearningbasedemergencyrescuepositioningtechnologyusingmatchingmapimages |