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Modern techniques in the Internet of Things or autonomous driving requiremore accuracy positioning ever. Classic location techniques mainly adapt tooutdoor scenarios, while they do not meet the requirement of indoor cases withmultiple paths. Meanwhile as a feature robust to noise and time variations,Channel State Information (CSI) has shown its advantages over Received SignalStrength Indicator (RSSI) at more accurate positioning. To this end, this paperproposes the neural network method to estimate the centimeter-level indoorpositioning with real CSI data collected from linear antennas. It utilizes anamplitude of channel response or a correlation matrix as the input, which canhighly reduce the data size and suppress the noise. Also, it makes use of theconsistency in the user motion trajectory via Recurrent Neural Network (RNN)and signal-noise ratio (SNR) information, which can further improve theestimation accuracy, especially in small datasize learning. These contributionsall benefit the efficiency of the neural network, based on the results withother classic supervised learning methods.

Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks