Recent technological achievements have made it low cost to realize indoor localization using the received signal strength (RSS) information from Wi-Fi signals. However, the current RSS-based indoor localization techniques have two major challenges: one is that the RSS signal is quite sensitive to channel conditions, and the other is that sufficient number of access points (APs) is needed to provide enough RSS measurements for guaranteeing good performance. To solve these problems, this paper proposes an adaptive compressive sensing (CS) based indoor localization method based on the IEEE 802.11 Wi-Fi standard. The novel feature of this method is to dynamically adjust both the dictionary and the sparse solution using an online dictionary learning (DL) technology so that the location solution can better match the real-time RSS scenario. Meanwhile, an improved approximate l0 norm minimization algorithm is presented to enhance sparse recovery speed and reduce the number of APs required by indoor localization systems. The effectiveness of the proposed scheme is demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance and reduces computation complexity.
"Online-Calibrated CS-Based Indoor Localization Over
IEEE 802.11 Wireless Infrastructure," Progress In Electromagnetics Research C,
Vol. 70, 73-81, 2016. doi:10.2528/PIERC16101602
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