Research And Implementation Of Wireless Localization Method Based On Semi-supervised Domain Adaptation

With the development of wireless technology,the acquisition of WiFi signals has become more convenient and faster,using WiFi signals to complete indoor WiFi localization tasks has gradually become a research hotspot.Due to the environmental sensitivity of the WiFi signal,when the indoor environment changes,the WiFi signal will change accordingly,resulting in the failure of the localization model.If the data is re-collected for training in a new environment,it will greatly consume time and labor costs.Therefore,this thesis studies and discusses the device-free localization problem after environmental changes,and proposes localization and adaptation methods that can cope with environmental changes.Firstly,this thesis improves the existing wireless localization system.Before wireless localization,the indoor presence judgment algorithm is used to determine whether indoor personnel localization can be carried out,and then the one-dimensional convolutional regression network is used to predict the coordinate,and achieved better localization effect than other networks.Aiming at the problem of environmental changes,this thesis first studies the impact of environmental changes on WiFi signals,and analyzes the reasons for the failure of the device-free localization model.Then in order to judge whether the environment changes,this thesis proposes an environment change detection algorithm based on autoencoder.And when the environment changes,an unsupervised domain adaptation solution is further proposed,which utilizes unlabeled data in the target domain to enable the localization model to adapt to the new environment by combining adversarial learning with distribution alignment.Finally,for the device-free localization problem after room changes,this thesis proposes a semi-supervised domain adaptation solution,which uses a small amount of location point with labelled data from new room to complete the distribution alignment of location points,so that the localization model can be migrated across rooms.In this thesis,the above algorithm is experimentally verified in different changing environments of different rooms.The experimental results show that the environmental change detection algorithm in this thesis can achieve a discrimination effect close to 100%for different environmental changes; the unsupervised domain adaptation algorithm can achieve an average localization error of 149 cm after different environmental changes in the room.The cross room migration scheme in this thesis achieves an average localization error of 181.5cm.At the same time,compared with other related domain adaptation algorithms,the localization effect of this thesis has also achieved better results,which shows the effectiveness of the proposed scheme in solving the device-free localization problem of environmental changes.According to the indoor localization requirements of smart home,this thesis designs and implements an indoor WiFi localization system.The system can locate the personnel in the indoor environment,detect the possible environmental changes,and adapt to the corresponding domain according to the environmental changes.Finally,the function and performance tests are completed for the system.