Abstract
With the increasing demand for location-based services, indoor fingerprinting localization based on received signal strength indicator or channel state information (CSI) has become an increasingly important technique due to its low hardware requirement and high accuracy. Due to robustness against the multipath effect, frequency domain CSI of orthogonal frequency division multiplexing systems is supposed to provide an excellent positioning measurement for indoor localization. In this paper, we propose a novel fingerprint localization method based on modified CSI using the Kalman Filter. For the offline stage, we use modified CSI to build a fingerprint database. In the online stage, we employ the K-nearest neighbor method for location estimation. The proposed indoor fingerprint localization scheme is implemented and validated with experiments in a representative indoor environment with commercial IEEE 802.11 NICs. Compared with existing methods, the experimental results demonstrate that the proposed method can effectively reduce positioning error.
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