Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization

With the recent development in mobile computing devices and as the ubiquitous deployment of access points(APs) of Wireless Local Area Networks(WLANs), WLAN based indoor localization systems(WILSs) are of mounting concentration and are becoming more and more prevalent for they do not require additional infrastructure. As to the localization methods in WILSs, for the approaches used to localization in satellite based global position systems are difficult to achieve in indoor environments, fingerprint based localization algorithms(FLAs) are predominant in the RSS based schemes. However, the performance of FLAs has close relationship with the number of APs and the number of reference points(RPs) in WILSs, especially as the redundant deployment of APs and RPs in the system. There are two fatal problems, curse of dimensionality (CoD) and asymmetric matching(AM), caused by increasing number of APs and breaking down APs during online stage. In this paper, a semi-supervised RSS dimensionality reduction algorithm is proposed to solve these two dilemmas at the same time and there are numerous analyses about the theoretical realization of the proposed method. Another significant innovation of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm to improve the localization accuracy of indoor localization. Comparing with LDE-KNN algorithm, SDE-KNN method is going to update RSS during online stage or offline stage. It is the update scheme that improves the performance of the proposed algorithm. After locally analyzing of parameters, the optimized value of each parameter could be gained. As it presents in by numeral analysis, the performance optimized parameters of SDE-KNN of it is overly better than initial KNN and LDE-KNN. The localization accuracy of SDE-KNN is higher than 70% as the error radius is 0.5 meter, and it is about 10 percent higher than initial KNN, as well as 25 percent higher than LDE-KNN. As the error radius equals to 1 meter, the proposed algorithm could gain over 95% of localization accuracy.