The study of using Particle Filter method combined Indoor Magnetic Map to support Pedestrian Dead Reckoning for Indoor Positioning.

碩士 === 國立政治大學 === 地政學系 === 107 === In the past, whenever we came to an unfamiliar environment, we often needed a map to guide us. With the appearance of Global Navigation Satellite System (GNSS), the outdoor positioning has approached perfection. However, due to the environment obstruction, the indo...

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Bibliographic Details
Main Authors: Chen, Yu-Chun, 陳宥竣
Other Authors: Ning, Fang-Shii
Format: Others
Language:zh-TW
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/7y53v5
Description
Summary:碩士 === 國立政治大學 === 地政學系 === 107 === In the past, whenever we came to an unfamiliar environment, we often needed a map to guide us. With the appearance of Global Navigation Satellite System (GNSS), the outdoor positioning has approached perfection. However, due to the environment obstruction, the indoor signal cannot be received for positioning. Therefore, indoor positioning technology has become the focus of research and development in recent years. In the history of indoor positioning, it mostly set up sensors to detect the position of the users, such as infrared positioning system. In recent years, most of the technologies send out signals actively, such as Wi-Fi, iBeacon, RFID, or using images, INS, and even less mentioned Magnetic field positioning technology. All the technologies above have their own advantages and disadvantages, and the cost directly affects the threshold of use of indoor positioning methods. Therefore, this study chose to use the mobile device to obtain information from the gyroscope and accelerometer to detect the path and estimate the user's position. Because the Pedestrian Dead Reckoning (PDR) technology will accumulate errors quickly with time, this study adds the concept of particle filter, combined with the indoor magnetic information to give particles appropriate weights to solve the problem, and achieve the purpose of indoor positioning within a reasonable margin of error. In addition, this study also changed the way estimating the user's step length, and proved the feasibility of the method proposed in this study. The research results show that the positioning accuracy can reach the level of 0.6 ~ 0.8 meters.