A Novel Algorithm for Fingerprinting Indoor Localization Based on K-Correlation Coefficient

Indoor localization can render a service of accurate position information, thus there is a widespread use in many fields. Correlation coefficient method and KNN(K-Nearest Neighbor) are two kinds of common localization algorithms based on fingerprinting database in Wi-Fi environment. But the positional accuracy of them is very finite and can't meet the requirement of accurate indoor localization. In this paper, we propose a K-Correlation Coefficient algorithm which combines the mean value correlation coefficient and KNN. Their localization advantages can be developed in a certain indoor environment by K-Correlation Coefficient, and the point of different values between Spearman Rank Correlation Coefficient and Pearson Correlation Coefficient is also solved satisfactorily. A fingerprinting database is established in the physical space. We choose multiple test points to detect the novel algorithm and the result shows localization accuracy of K-Correlation Coefficient is improved, 38.86% improved comparing with correlation coefficient method and 23.35% with KNN. Meanwhile the computing workload isn't increased and it can be used widely in indoor localization.