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Research On Low Power Bluetooth Indoor Positioning System Based On RSSI Ranging

Posted on:2020-08-22Degree:MasterType:Thesis
Country:ChinaCandidate:R R ShiFull Text:PDF
GTID:2428330572480087Subject:Electronic and communication engineering
Abstract/Summary:PDF Full Text Request
With the advancement of mobile Internet and Internet of things,many location service applications developed by satellite positioning core technologies are being integrated into people's life and are increasingly indispensable.However,people spend most of their daily life indoors,where weak satellite signals may cause great positioning deviation or even unavailability.Therefore,indoor positioning technology has been widely paid attention to,the emergence of the use of Bluetooth,WiFi and cellular mobile wireless communication technology such as indoor positioning technology.With the rapid development of Bluetooth technology in recent years,it has become the focus of indoor positioning technology research and development due to its low power consumption,networkable networking and wide popularity.The use of Bluetooth wireless signal location,facing different indoor complex environment to the random disturbance of Bluetooth signal propagation model optimization,Bluetooth signal preprocessing and feature selection,as well as the improvement of geometric model localization algorithm and other challenges.In this paper,the indoor environment with visual distance is taken as the experimental scene,and the algorithm research mainly includes the research of RSSI-based ranging model,the design of regional weighted filtering algorithm and the design of dynamic coefficient weighted centroid localization algorithm.In the research of RSSI-based ranging model,the RSSI values of Bluetooth signals at different propagation distances in the experimental environment are collected,and the linear regression model is used to determine the parameters of the ranging model by the principle of least squares.The RSSI data collected in the experimental environment were preprocessed by the designed regionally weighted-kalman filtering algorithm to improve the accuracy of the overall experimental results.The dynamic coefficient weighted centroid algorithm is based on the traditional weighted centroid localization algorithm,and by modifying its weight value setting method,the dynamic coefficient weight setting strategy is designed to improve the accuracy of the positioning result.At the end of this paper,the reference node and target node were designed on the hardware platform of CC2640r2f low-power Bluetooth development board to complete the construction of the experimental environment,and the data acquisition system was developed through the API interface of the Bluetooth protocol stack for the experimental data acquisition,thus realizing the verification of the algorithm in the paper.Experimental results show that the dynamic coefficient weighted centroid localization algorithm proposed in this paper improves the overall positioning accuracy by about 34%compared with the traditional weighted centroid localization algorithm,and the positioning performance is obviously improved.
Keywords/Search Tags:Bluetooth indoor positioning, RSSI-based ranging model, Regionally weighted-kalman filtering algorithm, Dynamic coefficient weighted centroid localization algorithm, Data acquisition system
PDF Full Text Request
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