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Research And Implementation Of Indoor Location Technology Based On Bluetooth

Posted on:2020-05-12Degree:MasterType:Thesis
Country:ChinaCandidate:X H HuFull Text:PDF
GTID:2428330602968368Subject:Computer technology
Abstract/Summary:PDF Full Text Request
With the rapid development of mobile Internet,the location based services become more and more important for human lives.Global Positioning System(GPS)can provide positioning in outdoors.However,because satellite signals are blocked by buildings,users cannot use GPS to localize themselves in indoors.The emergence of low power Bluetooth technology,with its low power consumption,easy installation,low cost,easy deployment and high positioning accuracy,quickly become a new approach for indoor localization research.In this paper,I design and implement an indoor positioning system which is based on Bluetooth and greatly improve the processing delay and positioning accuracy.This dissertation mainly addresses the following research issues:Firstly,the existing indoor positioning technologies(e.g.,WiFi,geomagnetic and Bluetooth)are well studied,and their advantages and disadvantages are drawn.Overall,iBeacon enabled by low power Bluetooth function is selected to realize positioning function.I focus on developing the indoor location algorithm,then decide to use the least square method to localize users in a long corridor.The Bluetooth signal is filtered.Through simulation experiments,the limiting filter,median average filter and Kalman filter are studied.Finally,the Kalman filter with the best performance is selected as the underline filter.The influence of height,environment change,facing direction and layout of beacon nodes on received signal strength indicator(RSSI)value is studied.Secondly,KNN(K Nearest Neighbor)indoor localization algorithm is studied.We use MATLAB to simulate KNN algorithm and analyze its error.The average error of KNN algorithm is 2.08 m.Then I implement an algorithm combining KNN and Kalman filter in MATLAB.The results show that the average positioning error is 1.86 m,which is 11% lower than that of KNN algorithm.Finally,the design and development of Android-based system,basic positioning function and path searching function are completed,and many experiments are conducted in the school laboratory building and dormitory building.The results show that our system can meet the basic requirements of positioning function.Furthermore,to make the community and nursing home more efficient and reasonable for elderly-care,I conduct many experiments in nursing homes.The functions of special area setting and special area timeout alarm are realized.
Keywords/Search Tags:Indoor positioning, Kalman filter, KNN, Low power Bluetooth, Matlab
PDF Full Text Request
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