Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. And also location fingerprinting in detail since it is used in most current system or solutions. This project focuses on the localization using Received Signal Strength (RSS) in dense multipath indoor environments. A dynamic system approach is proposed in the fingerprinting module, where the location is estimated from the state instead from RSS directly. The state is reconstructed from a temporal sequence of RSS samples by incorporating a proper memory structure based on Taken’s embedded theory. Then, a more accurate statelocation correlation is estimated because the impact of the temporal variation due to multipath is considered. General Terms Triangulation, scene analysis, proximity