許永真臺灣大學：資訊工程學研究所柯嘉南Ke, Chia-NanChia-NanKe2007-11-262018-07-052007-11-262018-07-052006http://ntur.lib.ntu.edu.tw//handle/246246/53961Wireless LAN 802.11x has become a popular infrastructure in most public buildings. Many location system have been developed based on the wireless LAN. It is convenient for indoor location-aware services. However, recent research has demonstrated that localization error signi‾cantly increases in crowded and dynamic situations due to electromagnetic interferences. We propose collaborative localization to enhance the accuracy in the situation of human clusters by leveraging more accurate location information from nearby neighbors. We de‾ne "con‾dence" to represent the measurement error of each user. We use the Kalman ‾lter, interacting multiple model, and particle ‾lter to evaluate the con‾dence score. Our results showed a clear inverse relationship between con‾dence score and measurement error. Using con‾dence score as weight and neighborhood information, we can revise each user's measurement, and obtain the more accurate location estimation. Our experiments showed 28:2 － 56:0% accuracy improvement over the baseline system Ekahau, a commercial WiFi localization system.Acknowledgments i Abstract iii List of Figures viii List of Tables xii Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 Preliminaries 5 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Bayes Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Interacting Multiple Model . . . . . . . . . . . . . . . . . . . . 17 2.1.4 Particle Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.1 Localization System . . . . . . . . . . . . . . . . . . . . . . . 23 2.2.2 Spring Relaxation . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 3 Con‾dence of Location Estimation 31 3.1 Overview of Collaborative Localization System . . . . . . . . . . . . . 31 3.2 Problem De‾nition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . 38 3.3 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Chapter 4 Implementation 43 4.1 WiFi-based Location System . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Implement Particle Filter . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3 Implement Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 46 4.4 Implement Interacting Multiple Model . . . . . . . . . . . . . . . . . 49 4.5 Evaluate Con‾dence . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Chapter 5 Experiments 55 5.1 Environment Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Measurement Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3 Filter Function Performance . . . . . . . . . . . . . . . . . . . . . . . 60 5.4 Con‾dence & Measurement Error . . . . . . . . . . . . . . . . . . . . 61 5.5 Location Estimation Performance . . . . . . . . . . . . . . . . . . . . 72 5.5.1 Stationary Scenario . . . . . . . . . . . . . . . . . . . . . . . . 72 5.5.2 Mobile Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Chapter 6 Conclusion 79 6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Bibliography 821082034 bytesapplication/pdfen-US可信度無線網路室內定位機率模組confidencewireless LANindoor localizationprobabilistic modelthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/53961/1/ntu-95-R93922109-1.pdf