El-Sheimy, NaserNiu, XiaojiLi, You2016-01-212016-01-212016-01-212016Li, Y. (2016). Integration of MEMS Sensors, WiFi, and Magnetic Features for Indoor Pedestrian Navigation with Consumer Portable Devices (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26584http://hdl.handle.net/11023/2765Mobile 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 %.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.GeodesyEngineering--AutomotiveEngineering--CivilRoboticsindoor positioningpedestrian navigationwireless positioningmagnetic featuresMEMS inertial sensorsdoctoral thesis10.11575/PRISM/26584