Location Fingerprint Discrimination Maximization for Indoor WLAN Access Point Optimization Using Fast Discrete Water-Filling

Abstract

Access Point (AP) optimization is one of the most important components in indoor Wireless Local Area Network (WLAN) localization technique since the AP number and locations have significant impact on the variations of Received Signal Strength (RSS) in target environment. Different from the conventional AP optimization approaches, we propose to use the concept of adaptive channel power allocation to construct a water-filling model, and then conduct AP optimization based on the weights of candidate AP locations which are calculated by the fast discrete water-filling algorithm. The experimental results demonstrate that the proposed approach is able to achieve high localization precision, and meanwhile consume low time overhead.