ResidualRanking: A robust access-point selection strategy for indoor location tracking

In this paper, we propose a robust approach to access point (AP) selection problem for the indoor location tracking. It takes the environments changes into account and makes use of residuals ranking algorithm to select those APs least sensitive to the environment changes in indoor location tracking, we call it ResidualRanking method, also we make an improvement of residual computing according to the properties of radio signals. Additionally, we present a location tracking system called BRR (Bayesian and Residuals Ranking) which is based on the Bayesian decision method and the ResidualRanking method we proposed. Finally, we make a comparison to the MaxMean AP selection method, and the experimental results indicate that the ResidualRanking method we proposed can achieve a better performance than the MaxMean method, also the proposed system BBR can get desirable results in the realist indoor location tracking.