A CalibrationFree Method Based on Grey Relational Analysis for Heterogeneous Smartphones in FingerprintBased Indoor Positioning The fingerprint method has been widely adopted in WiFi indoor positioning because of its advantage in nonlineofsight channels between access points (APs) and mobile users. However, the received signal strength (RSS) during the fingerprint positioning process generally varies due to the dissimilar hardware configurations of heterogeneous smartphones. This difference may degrade the accuracy of fingerprint matching between fingerprint and test data. Thus, this paper puts forward a fingerprint method based on grey relational analysis (GRA) to approach the challenge of heterogeneous smartphones and to improve positioning accuracy. Initially, the grey relational coefficient (GRC) between the RSS comparability sequence of each reference point (RP) and the RSS reference sequence of the test point (TP) is calculated. Subsequently, the grey relational degree (GRD) between each RP and TP is determined on the basis of GRC, and the K most relational RPs are selected in accordance with the value of GRD. Finally, the user location is determined by weighting the K most relational RPs that correspond to the coordinates. The main advantage of this GRA method is that it does not require device calibration when handling heterogeneous smartphone problems. We further carry out extensive experiments using heterogeneous Android smartphones in an office environment to verify the positioning performance of the proposed method. Experimental results indicate that the proposed method outperforms the existing ones no matter whether heterogeneous smartphones are used.

Received Signal StrengthBased Indoor Localization Using Hierarchical Classification Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. WiFi received signal strength (RSS)based indoor localization techniques have attracted extensive attentions because WiFi access points (APs) are widely deployed and we can obtain the WiFi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classificationbased method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved KMeans clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the KNearest Neighbor (KNN) algorithm and support vector machine (SVM) with the oneversusone strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in realworld environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.