Biblio

This paper deals with a new indoor localization scheme called “CEPHEID” by using ceiling lighting fixtures. It is based on the fact that each lighting fixture has its own characteristic flickering pattern. Then, the author proposes a technique to identify individual light by using simple instruments and DNN classifier. Thanks to the less requirements for hardware, CEPHEID can be implemented by a few simple discrete electronic components and an ordinary smartphone. A prototype “CEPHEID dongle” is also introduced in this paper. Finally, the validity of the author's method is examined by indoor positioning experiments.

Recent indoor positioning systems that utilize channel state information (CSI) consider ideal scenarios to achieve high-accuracy performance in fingerprint matching. However, one essential component in achieving high accuracy is the collection of high-quality fingerprints. The quality of fingerprints may vary due to uncontrollable factors such as environment noise, interference, and hardware instability. In our paper, we propose a method for collecting high-quality fingerprints for indoor positioning. First, we have developed a logistic regression classifier based on gradient descent to evaluate the quality of the collected channel frequency response (CFR) samples. We employ the classifier to sift out poor CFR samples and only retain good ones as input to the positioning system. We discover that our classifier can achieve high classification accuracy from over thousands of CFR samples. We then evaluate the positioning accuracy based on two techniques: Time-Reversal Resonating Strength (TRRS) and Support Vector Machines (SVM). We find that the sifted fingerprints always result in better positioning performance. For example, an average percentage improvement of 114% for TRRS and 22% for SVM compared to that of unsifted fingerprints of the same 40-MHz effective bandwidth.