Abstract:

Because wireless signals are susceptible to interference during the propagation process, the application of traditional indoor positioning methods in real life is limited.Because location-based fingerprint positioning technology has the advantage of strong universality, it has become a current research hotspot.The number of fingerprint data is an important factor affecting the accuracy of fingerprint positioning, but the cost of collecting a large amount of fingerprint data is large.Therefore, how to use a small amount of fingerprint data to achieve higher positioning accuracy is a difficult point of fingerprint positioning technology.Aiming at this problem, a high-precision indoor wireless positioning method based on generative adversarial network was proposed.Firstly, fingerprint data was collected densely at equal intervals indoors, and the initial fingerprint data set was constructed, the part of the fingerprint data was selected in the initial fingerprint data set, and the generative adversarial network was used to obtain a large amount of fingerprint data from part of the fingerprint data.Then, based on these generated data, a KNN (k-nearest neighbor) model and a random forest model were used for location prediction.Experimental results show that this method can achieve high wireless positioning accuracy based on a small amount of fingerprint data, and the positioning accuracy can reach 15.4 cm.

Key words: fingerprint localization, generative adversarial network, indoor, KNN, random forest