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Title: 以手機結合卷積神經網路深度學習實現室內位置追蹤
Smartphone-Based Indoor Position Tracking with CNN Deep Learning
Authors: 吳宛庭
Wu, Wan-Ting
Contributors: 蔡子傑
Tsai, Tzu-Chieh
Wu, Wan-Ting
Keywords: 深度學習
Deep Learning
Indoor Localization
Indoor Position Tracking
Convolutional Neural Network
Smartphone Sensor
Date: 2021
Issue Date: 2021-03-02 14:56:47 (UTC+8)
Abstract: GPS訊號因涵蓋範圍擴及全球,目前被廣泛運用於室外定位,而室內環境由於缺乏訊號覆蓋,無法獲得良好的定位效果。因此過去幾年許多學者致力於各種室內定位研究,包含使用感測器或信號發射裝置,以數據差異或信號強弱辨別使用者所在位置,而這些通常需要事先架設感測器,及量測信號地圖。現今生活中手機方便性極高,若只使用手機內感測器進行室內位置追蹤,是我們認為最便利且直觀的方式。
GPS signals are widely used for outdoor localization due to their worldwide coverage, however, may not be applicable for indoor environments to achieve good localization results. Over the past year, there were many indoor localization studies, including the use of various sensors or signal strengths to identify the location of users or devices. These usually require infrastructure and signal measurements in advance. Nowadays, for convenience, if only sensors in smartphones are used for indoor localization, it will be the most intuitive way.
In recent years, "AI artificial intelligence" technology has been developing rapidly. With the increasing computing speed of information appliances, it becomes much easier to develop the related applications with the framework of deep learning. In this thesis, we propose a deep learning method. This method uses the acceleration sensor information in a smartphone as the image data sample, and uses the convolutional neural network model for sample training. We further develop an average mechanism for prediction of step lengths. By combining with compass data from the smartphone, it can track the user's indoor location. We deploy our model on the smartphone APP which can display the route in real time while we walk. We tested it in the building of NCCU for users of different heights. Experiments results are quite satisfactory with only 1.8% error.
Reference: [1]Paramvir Bahl, & Venkata N. Padmanabhan. (2000, March). RADAR: An in-building RF-based user location and tracking system, Infocom Nineteenth Joint Conference of the IEEE Computer & Communications Societies IEEE, Tel Aviv, Israel.
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[3]Estefania Munoz Diaz, & Ana Luz Mendiguchia Gonzalez. (2014, October). Step Detector and Step Length Estimator for an Inertial Pocket Navigation System, 2014 International Conference on Indoor Positioning and Indoor Navigation, Busan, South Korea. doi:10.1109/IPIN.2014.7275473
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[6]Yi-Shan Li, & Fang-Shii Ning. (2018 December). Low-Cost Indoor Positioning Application Based on Map Assistance and Mobile Phone Sensors, Sensors, 18(12), 4285. MDPI AG. Retrieved from
[7]Baoding Zhou,Jun Yang , & Qingquan Li. (2019). Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network. Sensors, 19(3), 621. MDPI AG. Retrieved from
[8]Ayush Mittal, Saideep Tiku, & Sudeep Pasricha. (2018). Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices. GLSVLSI '18: Great Lakes Symposium on VLSI 2018, 117–122.
[9]Jiheon Kang, Joonbeom Lee, & Doo-Seop Eom. (2018). Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization. Sensors, 18(9), 3149. MDPI AG. Retrieved from
Description: 碩士
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Data Type: thesis
Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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