A Real-time Clustering Scheme using Coordinate Density for 3D Indoor Localization

The demands for indoor localization in the field of automation is growing very rapidly. Typical examples include object positioning, object trajectory, safety rescue management and so on. Even though the trilateration is generally used to indoor localization, the quality of detected data is greatly affected by the surrounding radio frequency and obstacle condition, so it cause a lot of errors in data. In this paper, we proposed a real-time clustering scheme using the coordinate density to reduce this problem in 3D indoor localization. It was confirmed that the proposed clustering scheme can significantly reduce the localization error in indoor obstacle environments.