Radio Map Efficient Building Method Using Tensor Completion for WLAN Indoor Positioning System

Wireless local area network (WLAN) fingerprint-based indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations. However, huge labor and time are needed to establish the fingerprint database called radio map. To address this problem, we propose an efficient radio map building method using tensor completion. The cost of the proposed method is reduced by lessening the number of reference points (RPs) to be measured. Due to the strong correlation between data, estimating received signal strength (RSS) at unmeasured reference points can be formulated as a low rank tensor completion problem. And the issue can be transformed into a convex optimization problem by substituting rank operation with trace norm operation. We solve the problem by employing the alternating direction method of multipliers (ADMM) algorithm. The experiment results indicate that the proposed method can not only reduce the effort of radio map building remarkably, but also achieve high positioning accuracy.