A New Indoor Localization Algorithm Using Received Signal Strength Indicator Measurements and Statistical Feature of the Channel State Information

Since indoor location information can be utilized in many aspects, indoor localization using existing wireless network has received much attentions. In this paper, a novel indoor localization algorithm using received signal strength indicator (RSSI) measurements and statistical feature of the channel state information (CSI) is proposed. In the off-line phase, with the obtained CSI measurement, the principal component analysis (PCA) pre-processing is utilized for dimension reduction at first. Then, the statistical features of the CSI measurements are extracted. Next, the RSSI measurements, the extracted feature of CSI measurements and the reference positions form the training data set. At last, the support vector machine (SVM) technique is proposed for regression learning and obtain the position based regression function. In the on-line phase, after PCA pre-processing and feature extraction of CSI measurements, the final position can estimated straightly with the regression function. The experiment results are shown that the proposed algorithm can offer more accurate localization result than other existing algorithms.