RFID indoor localization based on support vector regression and k-means

Systems need to know the physical locations of objects and people to optimize user experience and solve logistical and security issues. Also, there is a growing demand for applications that need to locate individual assets for industrial automation. This work proposes an indoor positioning system (IPS) able to estimate the item-level location of stationary objects using off-the-shelf equipment. By using RFID technology, a machine learning model based on support vector regression (SVR) is proposed. A multi-frequency technique is developed in order to overcome off-the-shelf equipment constraints. A k-means approach is also applied to improve accuracy. We have implemented our system and evaluated it using real experiments. The localization error is between 17 and 31 cm in 2.25m
²
area coverage.