Research On Indoor Localization Method Based On Deep Transfer Learning

With the vigorous development of Internet of things technology,the market demand based on indoor localization services is growing with each passing day.The fingerprint localization method in indoor localization is widely used because of its strong ability for resistance to disturbance in complex indoor environments.The traditional fingerprint localization algorithm assumes that the signal strength distribution in the offline database construction phase is consistent with that in the online localization phase.However,the distribution of the signal strength often changes due to environmental changes,heterogeneous devices and other factors.In addition,the lack or addition of wireless access points will lead to heterogeneous feature dimensions.By transferring the knowledge from the source domain to the target domain,transfer learning can reduce the change of distribution of the signal strength caused by environmental changes and heterogeneous devices.However,the traditional transfer learning localization algorithm does not fully extract the transferable features,which leads to insufficient knowledge transfer.To solve this problem,this thesis studies the indoor localization technology based on deep transfer learning.The main research work is as follows:Aiming at the problem of insufficient knowledge transfer in existing transfer learning localization algorithms,this thesis proposes a transfer learning localization algorithm based on global and local structural consistency constraints.In the potential subspace,by minimizing the discrepancy of marginal and conditional probability distribution between the source and target domain,and maximizing the variance of the two domains,the consistency of the global structure is constrained,so that the mean difference between the domains is reduced and the samples are separated as much as possible after mapping;by minimizing the intra class variance and maximizing the inter class variance,and maintaining the local neighborhood structure of the data,the consistency of the local structure is constrained,so that the mapped samples with the same label are as close as possible,and the samples with different labels are as far away as possible,and the neighborhood relationship of the data before and after transferring is preserved.Experimental results verify the effectiveness of the algorithm.In this thesis,an indoor localization algorithm based on homogeneous deep transfer network is proposed,which can learn the deep transferable features reflecting the invariable factors between domains to complete the knowledge transfer by the deep neural network,and overcomes the shortcomings of traditional transfer learning localization algorithm,which only learn the shallow representation features through the shallow mapping and can not transfer the knowledge fully.The deep transfer network can reduce the localization performance degradation caused by the change of distribution of the signal strength caused by the change of environment and heterogeneous devices by matching the mean embedding between domains and the mean embedding between related subdomains in Hilbert space,and the covariance between domains.The experimental results verify the effectiveness of the deep transfer network.Aiming at the problem that traditional heterogeneous transfer learning localization algorithm can not transfer knowledge sufficiently by learning shallow common features through shallow linear mapping,this thesis proposes an indoor localization algorithm based on heterogeneous deep transfer forest.By collecting a small amount of labeled data in the target domain,two deep neural networks are used as feature mapping layers to learn a common feature of the source domain and the target domain respectively.The probabilistic decision forest can retain the characteristics of data structure,and it is used as the prediction layer for joint transferring and localization.The experimental results show that the localization performance of the algorithm is better in the situation of different distribution between domains and different feature dimensions.