A probabilistic approach to outdoor localization using clustering and principal component transformations

Abstract

A probabilistic approach for outdoor location estimation using GSM received signal strength (RSS) from base stations (BSs) is presented. The proposed approach first divides the region of interest into different clusters based on deviations from the path loss model for each RSS component. In each cluster, the proposed algorithm uses principal component analysis (PCA) to intelligently transform RSS into new uncorrelated dimensions. This retains accuracy by not losing the substantial RSS correlations in each cluster, but also accommodates the different RSS distributions in each cluster. Our experiments are conducted in a real GSM outdoor environment. The proposed approach is compared with a traditional probabilistic algorithm for three different area partitioning methods. The experimental results show that the positioning accuracy is significantly improved and our clustering scheme gives good support for location estimation. Furthermore, it also can be concluded that the clustering scheme created by using deviation RSS based on Mahalanobis distance performs better than that using deviation based on Euclidean distance in a complex environment. What's more, the proposed method can reduce the number of training data used while maintaining the accuracy required.