Indoor Wi-Fi positioning algorithm based on combination of Location Fingerprint and Unscented Kalman Filter

Wireless indoor positioning systems have gained more popularity in recent years by taking the advantage of already existing radio infrastructures, which saves extra deployment costs and effort. These systems have been widely used in location tracking, helping people to navigate and use logistics systems etc. Global Positioning System (GPS) works well outside, where there is a direct line of sight between transmitter and receiver, but they lack positioning accuracy in indoor venues, where multipath propagation and complex building structures are big challenges for achieving a certain level of accuracy. Therefore, wireless technology can be used for localization in indoor venues, which does not require direct line of sight communication. Wi-Fi based indoor location systems can achieve low cost and high accuracy at the same time. The most widely used methods in indoor environments based on Wi-Fi are Trilateration technique and Location Fingerprint (LF) technique. Trilateration technique is more noise sensitive than LF technique, while the accuracy of Location Fingerprint technique is greatly affected by a change in environment. In this paper we propose a hybrid structure, the combination of both techniques, which shows that our algorithm can achieve a certain level of accuracy and robustness. The performance of our algorithm has been tested in different scenarios, depending on the path of human motion using Dead Reckoning method (DR), which justify that our proposed algorithm has performed better and achieved a reasonable level of accuracy.