An Indoor Localization Algorithm Based on Continuous Feature Scaling and Outlier Deleting

In this paper, a received signal strength indicator (RSSI) based indoor localization system is implemented employing Wi-Fi infrastructure. In the light of the feature-scaling-based k-nearest neighbor (FS-kNN) algorithm, a new continuous-feature-scaling model is proposed, which uses continuous weights instead of the discrete weights used in the FS-kNN, and needs not divide the entire RSSI space into the intervals. This gridless scheme avoids the difficulty of the weight selection at the common boundary of the adjacent intervals that could meet in the grid-based method of FS-kNN, which needs to divide the RSSI space into the intervals, ahead. An outlier deleting procedure is further used to improve the accuracy of the localization system. Experimental results indicate that the proposed method can be with a small localization error and is superior to some previous methods. One of the experiments achieves 1.34 m of the indoor localization error in a 12 m × 8 m area. The other is with 1.72 m of the indoor localization error in a 30 m × 25 m area. The proposed method performances best among the counterparts in the experiments.