Development of Wi-Fi and bluetooth based indoor positioning system with artificial intelligence techniques

Positioning systems are used to locate and guide people and objects. Especially in large enterprises, production lines and material warehouses due to the ease of product tracking and workflow management, the correct positioning of objects in indoor areas is gaining importance day by day. We are looking for solutions for indoor positioning systems using wireless technologies for interoperable systems that offer global solutions. Within the scope of this thesis, positioning technologies, algorithms and signal measurement techniques frequently used in the literature are presented by examining the approaches for closed area positioning systems. Wi-Fi and Bluetooth technologies and positioning systems have been developed for indoor use. For this purpose, devices have been developed for field analysis, node and wireless monitoring system and performance tests have been performed on a closed farm with these devices. In the positioning systems, signal measurements were made with RSSI technique and the signal map of the working area was modeled by fingerprint method. In order to increase the positioning accuracy, K-nearest neighbor and artificial neural networks, one of the artificial intelligence techniques, were used in the modeling of the signal map. Detailed performance results of the actual position estimates are shown on the graphs.