Emmanuel O. Agu
The development in wireless technology, mobile smart devices and Internet of Things has gave birth to a booming era or the wireless indoor geolocation. This technology have been increasingly used within our daily life and help people to build up the tracking system which could be used by fulfillment centers and grocery stores. To achieve higher localization accuracy with wireless geolocation, we need a higher density of deployment which involves high deployment and maintenance cost. To balance the accuracy and the cost, people have begun using wireless localization employing inertial navigation system (INS) which provide speed and direction of movement. When we combine Radio Frequency (RF) localization with INS, we have a hybrid INS/RF localization system which can achieve high localization accuracy with low cost. In this thesis, we use accelerometers and magnetometers in an Android smart phone to build a hybrid INS/RF system and use two different technologies for RF localization: Radio Frequency Identification Device (RFID) and Wi-Fi. Using this system, we conducted measurements of the hybrid localization system and evaluate its performance. The specific contributions of the thesis are: (1)Empirical performance evaluation of the INS/RFID localization system. It relates the localization error to the number and position of RFID tags. (2)Model the effect of metallic objects on accuracy of magnetometer. The model shows the relation between direction error and distance to metallic component. (3)Model shadow fading in close proximity of RF transmitter. It builds a distance dependent shadow fading model. (4)Model based performance evaluation of hybrid localization. The test bench uses our models to simulate the hybrid localization data.
Worcester Polytechnic Institute
Electrical & Computer Engineering
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Liu, Guanxiong, "Modeling and Performance Analysis of Hybrid Localization Using Inertial Sensor, RFID and Wi-Fi Signal" (2015). Masters Theses (All Theses, All Years). 514.
smartphone based localization, wireless localization, hybrid localization