  Modern indoor localization techniques are essential to overcome the weak GPS
coverage in indoor environments. Recently, considerable progress has been made
in Channel State Information (CSI) based indoor localization with signal
fingerprints. However, CSI signal patterns can be complicated in the large and
highly dynamic indoor spaces with complex interiors, thus a solution for
solving this issue is urgently needed to expand the applications of CSI to a
broader indoor space. In this paper, we propose an end-to-end solution
including data collection, pattern clustering, denoising, calibration and a
lightweight one-dimensional convolutional neural network (1D CNN) model with
CSI fingerprinting to tackle this problem. We have also created and plan to
open source a CSI dataset with a large amount of data collected across complex
indoor environments at Colorado State University. Experiments indicate that our
approach achieves up to 68.5% improved performance (mean distance error) with
minimal number of parameters, compared to the best-known deep machine learning
and CSI-based indoor localization works.
