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This study 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 state-location correlation is estimated because the impact of the temporal variation due to multipath is considered. An indoor experiment in Wireless Local Area Networks (WLAN) shows the effectiveness of our approach.