Radio Map Building Method Using Tensor Recovery For WLAN Indoor Positioning System

With the rapid development of communication technologies and smart devices,there is an increasing demand for high-precision positioning applications.WLAN indoor positioning system has a wide application prospect because it supplies good positioning performance without the requirement of additional hardware installations.However,in the traditional Radio Map building method,reference points are set in high density and sampled multiple times.That results in a huge labor and time cost and impedes the popularization of fingerprint-based WLAN indoor positioning system.Therefore,it is worth researching on how to build Radio Map in an efficient way.Nowadays,crowdsourcing provides a new idea for solving the high cost of Radio Map building.However,there are also problems to be solved.Firstly,the traditional reference point expansion method is affected by the order of data.Secondly,differences in user habits and changes in environment will introduce a lot of noise to Radio Map.To address these problem,we propose a reference point expansion algorithm and a noise reduction algorithm.This paper has achieved the following innovative results:(1)To address the problem that traditional reference point expansion algorithm is affected by the order of data,we propose a reference point expansion algorithm based on tensor completion.We formulate the issue as a low rank tensor completion problem and solve it by employing the BCD algorithm and the ADMM algorithm.Compared with the traditional method,the proposed method can be unaffected by the order of data and obtain the optimal solution.Therefore,it can improve the accuracy of Radio Map and the positioning accuracy of fingerprint-based WLAN indoor positioning system.(2)To address the problem that noise interference are introduced to crowdsourced data,we propose a noise reduction algorithm based on tensor recovery.We formulate the issue as a joint optimization problem with the low rank of the signal space and the sparseness of the noise space and solve it by employing the BCD algorithm and the ADMM algorithm.Compared with noise reduction method based on matrix recovery,the proposed method can make full use of the correlation between data and reduce the noise in Radio Map effectively.Therefore,it can improve the positioning accuracy of fingerprint-based WLAN indoor positioning system.