An Efficient Machine Learning Approach for Indoor Localization

摘要

Indoor localization has gained much attention over several decades due to enormous applications. However, the accuracy of indoor localization is hard to improve be-cause the signal propagation has small scale effects which leads to inaccurate measure-ments. In this paper, we propose an efficient learning approach that combines grid search based kernel support vector machine and principle component analysis. The proposed approach applies principle component analysis to reduce high dimensional measurements. Then we design a grid search algorithm to op-timize the parameters of kernel support vector machine in order to improve the localization accuracy. Experimental results indicate that the proposed approach reduces the localization error and improves the computational efficien-cy comparing with K-nearest neighbor, Back Propagation Neural Network and Support Vec-tor Machine based methods.