Open AccessDeFe: indoor localization based on channel state information feature using deep learning
Author(s) -
Xiandi Li,
Jingshi Shi,
Jianli Zhao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1303/1/012067
Subject(s) - computer science , channel state information , feature (linguistics) , fingerprint (computing) , probabilistic logic , artificial intelligence , pattern recognition (psychology) , channel (broadcasting) , signal (programming language) , artificial neural network , deep learning , restricted boltzmann machine , interface (matter) , data mining , layer (electronics) , computer network , wireless , telecommunications , philosophy , linguistics , bubble , maximum bubble pressure method , parallel computing , programming language , chemistry , organic chemistry
With the development of mobile devices, more and more location-based services (LBS) on the devices are needed and fingerprint indoor localization has become one most important technique because of its low cost and high accuracy. In this paper, we use the fingerprinting method which based on Channel State Information (CSI) for indoor localization. Furthermore, we extract the raw phase information from the multiple antennas and multiple sub-carriers through the IEEE 802.11n network interface card (NIC 5300) on several special models. Then we extract the required phase information and introduce two methods of mathematical statistics to analyse the feature of CSI signals. We replace the processed phase information with the signal features obtained from the analysis. For the offline stage, we employ a deep network with three hidden layers to train the signal features data, and use weights to represent fingerprints. Introducing a greedy learning algorithm to train the weights layer-by-layer to reduce the computational complexity, and the sub-network between two continuous layers forms a restricted Boltzmann machine (RBM). For the online location estimation, we use a probabilistic method based on the radial basis function (RBF). The neural network method we used this time is tested under two different scenarios, and different data are compared. The final conclusion is that the new method we combined is superior to the previous.