Energy Efficient Access Point Selection and Signal Projection for Accurate Indoor Positioning

We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network (WLAN)environment.First,we intelligently and jointly select the subset of Access Points (APs) used in positioning via Maximum Mutual Information (MMI) criterion.Second,we propose Orthogonal Locality Preserving Projection (OLPP) to reduce the redundancy among selected APs.OLPP effectively extracts the intrinsic location features in situations where previous linear signal projection techniques failed to do,while maintaining computational efficiency.Third,we show that the combination of AP selection and OLPP simultaneously exploits their complementary advantages while avoiding the drawbacks.Experimental results indicate that,compared with the widely used weighted K-nearest neighbor and maximum likelihood estimation method,the proposed method leads to 21.8％ (0.49 m) positioning accuracy improvement,while decreasing the computation cost by 65.4％.