Çoklu Veri Füzyonu Tabanlı İç Ortam Konumlandırma ve Takip Sistemi Tasarımı
Kaya, Sertaç Buğra
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In this thesis, indoor localization problem is investigated using received signal strength indicator and acceleration measurements. After localization with received signal strength and accelerometer separately, indoor localization is studied with the fusion of these two measurements with Kalman filter. In studies with received signal strength, path loss exponent is calculated according to possible different antenna pattern intersections. Localization of a static and dynamic target with generated signals that have noise with different standard deviation is studied in a simulation environment. Position is calculated by using integration of the accelerometer data. In accelerometer based positioning, two accelerometer models with two different algorithms are studied to obtain velocity and position. The speed data obtained using the selected algorithm is used as an extra input in the data fusion. Extended Kalman filter is applied to use the acceleration and received signal strength measurements in the data fusion. The values of the process and measurement noise used in the Extended Kalman filter are explained. Also the state transition matrix and measurement matrices used in thesis are specified. The effect of process noise on the estimation performance of Extended Kalman filter with noise and no noise motion is studied. In addition to acceleration and received signal strength in extended Kalman filter, the effect of inserting speed data into fusion is analyzed. The estimation performance of adaptive Kalman filter with unknown noise characteristics of measurement model and the position estimation performance of the Kalman filter with variable process noise are studied. Extended Kalman filter and the adaptive extended Kalman filter's position estimation performance in variable covariance values in received signal strength, acceleration and velocity data were compared by simulations. In the thesis study, a test set up is designed to collect empirical data and to observe the results obtained by simulations in indoor environment. The interfaces of the hardware components and components used in the test setup are explained. Real-time software that measures the signal strength between anchor nodes and beacon node in the system is developed. Raw data that is obtained from the accelerometer and nodes is sent to the data recorder with the critical frequency by means of a real time software. The inferences obtained from the simulations are applied on the empirical data and the location estimation results on empirical data are shown.