/Data61_Reducing-Ranging-Error-of-Pass-Loss-Model-in-Indoor-Localisation-Using-Clustering-Analysis

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Reduce-Ranging-Error-of-Pass-Loss-Model-in-Indoor-Localisation-Using-Clustering-Analysis

This short research project is part of a large indoor localisation project at CISRO.

It uses clustering analysis to reduce the range error of pass loss model in the in-cabin localisation.

Clustering.m is used to clustering K1, K2 in the pass loss model into clusters.

graphDrawing.m draws the Probability Density Function (PDF) of which cluster does one transmitter and receiver pairs belongs to.

If you are interested in this project, do not hesitate to contact me via email: chenhao.huang@sydney.edu.au

Thanks!