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|Title:||Integration of MEMS Sensors, WiFi, and Magnetic Features for Indoor Pedestrian Navigation with Consumer Portable Devices|
|Abstract:||Mobile location based services is attracting the public attention due to their potential applications in a wide range of personalized services. A demanding issue is to provide a trustworthy indoor navigation solution. This thesis provides a continous and smooth navigation solution by using off-the-shelf sensors in consumer portable devices, local magnetic features, and existing WiFi infrastructures. The main innovation points are: (a) It presents a real-time calibration method for gyro sensors in consumer portable devices. Through the use of multi-level constraints, this method happens automatically without the need for external equipment or user intervention, and reduced gyro biases from several deg/s to 0.15 deg/s indoors and 0.1 deg/s outdoors under natural human motions and in indoor environments with frequent magnetic interferences. (b) It introduces and evaluates two quality-control mechanisms for the integration of dead-reckoning (DR) and magnetic matching (MM), including a threshold-based method and an adaptive Kalman filter based method. The DR/MM results were enhanced by 47.6 % - 67.9 % and 43.9 % - 65.4 % in two environments through the use of quality control. (c) It presents a profile-based WiFi fingerprinting algorithm by using the short-term trajectories from DR and geometrical relationships of various reference points in the space. The use of the profile-based approach reduced WiFi fingerprinting errors by 14.0 %, and mitigated the WiFi mismatches when the user started navigation. (d) It proposes a WiFi-aided MM algorithm, which reduces both the mismatch rate and computational load. The WiFi-aided MM results were 70.8 % and 74.5 % more accurate than MM in two indoor environments, and 10.0 % and 10.5 % better than WiFi. (e) It designs and evaluates two improved DR/WiFi/MM integration structures and corresponding quality-control mechanisms. Structure #1 utilizes the WiFi-aided MM algorithm, while Structure #2 uses the integrated DR/WiFi solutions to limit the MM search space. This mechanism in Structure #2 has at least one more level than those in previous DR/WiFi/MM structures. The difference between the Structure #2 results in two indoor environments were 13 %, and the difference between the Structure #2 results under four different motion conditions were 16 %.|
|Appears in Collections:||Electronic Theses|
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|ucalgary_2015_Li_You.pdf||Main thesis||4.09 MB||Adobe PDF||View/Open|
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