Multiuser Collaborative Localization based on Inter-user Distance Estimation using Wi-Fi RSS Fingerprints

Indoor localization based on Wi-Fi received signal strength (RSS) fingerprints has been widely studied in recent years, mainly focusing on how to improve localization accuracy in an independent way. Some studies propose to use additional hardware devices to measure the distance in between users to improve localization accuracy, but these methods suffer from high cost and low practicality. In order to solve this problem, an inter-user distance estimation algorithm iDE is proposed in this paper. We first construct user features based on Wi-Fi fingerprints, then train the random forest and nearest neighbor regressors to obtain inter-user distance estimates, and design a multi-layer perceptron to fuse them. We propose a multiuser collaborative localization MCLoc based on inter-user distance estimation. It takes the distance estimation from iDE as a soft constraint to optimize the user's location using gradient descent search. Experiments in real scenarios show that in terms of inter-user distance estimation, the iDE algorithm can reduce the error by 24.2% compared with the single-model algorithm; in terms of positioning performance, the MCLoc algorithm can reduce the localization error by 11.4% compared with the non-collaborative method.