LAUSR

Wi-Fi fingerprint-based indoor positioning systems have extensive application prospects. Indoor positioning based on channel state information (CSI) becomes a research hotspot because it can provide fine-grained information. This paper proposes the Adaboost positioning system (ABPS) that uses the phase information in CSI and the ensemble learning (EL) method to train the fingerprint map. In this system, the abnormal phase data is eliminated by density-based clustering and the remainder is linearly transformed to build the fingerprint map. Through continuous iteration with the Adaboost algorithm, the sample weights of the training sets are continuously adjusted to prepare for classification. Finally, it can achieve position coordinates regression by means of confidence level. A series of experiments have been conducted to illustrate the effects of EL parameters, such as the number of iterations, the maximum depth of decision tree, the maximum number of features, and the minimum number of samples required for node subdivision. Meanwhile, the influence of input data sets size is investigated in an open environment and a complex laboratory environment. The experimental results of various classification and regression methods are also discussed, which validates the effectiveness of the proposed method.