Multi-Device Indoor Positioning System based on wireless fingerprinting system

  • 吳 汶峻

Student thesis: Doctoral Thesis


In this paper we use multi-device cooperation for indoor positioning based on Blue-tooth wireless signals The method we propose is to establish the fingerprint database first and then use the machine learning which choose KNN model to make a preliminary actual location prediction Since only use the KNN model for indoor positioning it cannot know the previous position and the information on the map to make more accurate predictions Therefore we have also added particle filter which can record the past position and map information to make indoor positioning more accurate Moreover in our system we propose the Tightly-Coupled Fusion the Loosely-Coupled Fusion and the Joint Particle Filtering These three methods are based on multiple devices to improve indoor positioning The Tightly-Coupled Fusion has combined multi-devices to determine the K value of KNN model then do KNN model and particle filter; The methods of the Loosely-Coupled Fusion and the Joint Particle Filtering are that multi-devices are separately determined to determine the K value of KNN model and then do particle filter The Joint Particle Filtering difference from the Loosely-Coupled Fusion is that all candidate positions of the KNN model are referenced instead of only two reference positions determined by KNN model We can decide which method to use according to different situations and let our system be more flexible The performance and positioning accuracy of our system is almost better than only a single device
Date of Award2020
Original languageEnglish
SupervisorSok-Ian Sou (Supervisor)

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