A Deep Learning based Approach for Indoor Localization A short review for “CSI-based fingerprinting for indoor localization: A deep learning
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Carsten Griwodz | S. Mao | Xiaohu Ge | X. Wang | L. Gao
[1] José L. Herrera,et al. Automatic Depth Extraction from 2D Images Using a Cluster-Based Learning Framework , 2018, IEEE Transactions on Image Processing.
[2] Hans-Peter Seidel,et al. Perceptual Real-Time 2D-to-3D Conversion Using Cue Fusion , 2018, IEEE Trans. Vis. Comput. Graph..
[3] Shiwen Mao,et al. BiLoc: Bi-Modal Deep Learning for Indoor Localization With Commodity 5GHz WiFi , 2017, IEEE Access.
[4] Shiwen Mao,et al. CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.
[5] David Akopian,et al. Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges , 2016, IEEE Communications Surveys & Tutorials.
[6] Yunhao Liu,et al. From RSSI to CSI , 2013, ACM Comput. Surv..
[7] Cheng-Xiang Wang,et al. Spatial Spectrum and Energy Efficiency of Random Cellular Networks , 2015, IEEE Transactions on Communications.
[8] Shiwen Mao,et al. CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.
[9] Xiangyu Wang,et al. RF Sensing in the Internet of Things: A General Deep Learning Framework , 2018, IEEE Communications Magazine.
[10] Qionghai Dai,et al. Depth map generation for 2D-to-3D conversion by limited user inputs and depth propagation , 2011, 2011 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON).