@inproceedings{10.1145/3292500.3330695,
author = {Lin, Ming and Song, Xiaomin and Qian, Qi and Li, Hao and Sun, Liang and Zhu, Shenghuo and Jin, Rong},
title = {Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3330695},
doi = {10.1145/3292500.3330695},
abstract = {Satellite-based positioning system such as GPS often suffers from large amount of
noise that degrades the positioning accuracy dramatically especially in real-time
applications. In this work, we consider a data-mining approach to enhance the GPS
signal. We build a large-scale high precision GPS receiver grid system to collect
real-time GPS signals for training. The Gaussian Process (GP) regression is chosen
to model the vertical Total Electron Content (vTEC) distribution of the ionosphere
of the Earth. Our experiments show that the noise in the real-time GPS signals often
exceeds the breakdown point of the conventional robust regression methods resulting
in sub-optimal system performance. We propose a three-step approach to address this
challenge. In the first step we perform a set of signal validity tests to separate
the signals into clean and dirty groups. In the second step, we train an initial model
on the clean signals and then reweigting the dirty signals based on the residual error.
A final model is retrained on both the clean signals and the reweighted dirty signals.
In the theoretical analysis, we prove that the proposed three-step approach is able
to tolerate much higher noise level than the vanilla robust regression methods if
two reweighting rules are followed. We validate the superiority of the proposed method
in our real-time high precision positioning system against several popular state-of-the-art
robust regression methods. Our method achieves centimeter positioning accuracy in
the benchmark region with probability $78.4%$ , outperforming the second best baseline
method by a margin of $8.3%$. The benchmark takes 6 hours on 20,000 CPU cores or 14
years on a single CPU.},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
pages = {2838–2847},
numpages = {10},
keywords = {real-time signal enhancement, gps, sensor grid, obust regression},
location = {Anchorage, AK, USA},
series = {KDD '19}
}