Crowdsource-based signal strength field estimation by Gaussian processes. Santos, I. & Djurić, P. M. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1215-1219, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex We address the problem of estimating a spatial field of signal strength from measurements of low accuracy. The measurements are obtained by users whose locations are inaccurately estimated. The spatial field is defined on a grid of nodes with known locations. The users report their locations and received signal strength to a central unit where all the measurements are processed. After the processing of the measurements, the estimated spatial field of signal strength is updated. We use a propagation model of the signal that includes an unknown path loss exponent. Furthermore, our model takes into account the inaccurate locations of the reporting users. In this paper, we employ a Bayesian approach for crowdsourcing that is based on Gaussian Processes. Unlike methods that provide only point estimates, with this approach we get the complete joint distribution of the spatial field. We demonstrate the performance of our method and compare it with the performance of some other methods by computer simulations. The results show that our approach outperforms the other approaches.
@InProceedings{8081401,
author = {I. Santos and P. M. Djurić},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Crowdsource-based signal strength field estimation by Gaussian processes},
year = {2017},
pages = {1215-1219},
abstract = {We address the problem of estimating a spatial field of signal strength from measurements of low accuracy. The measurements are obtained by users whose locations are inaccurately estimated. The spatial field is defined on a grid of nodes with known locations. The users report their locations and received signal strength to a central unit where all the measurements are processed. After the processing of the measurements, the estimated spatial field of signal strength is updated. We use a propagation model of the signal that includes an unknown path loss exponent. Furthermore, our model takes into account the inaccurate locations of the reporting users. In this paper, we employ a Bayesian approach for crowdsourcing that is based on Gaussian Processes. Unlike methods that provide only point estimates, with this approach we get the complete joint distribution of the spatial field. We demonstrate the performance of our method and compare it with the performance of some other methods by computer simulations. The results show that our approach outperforms the other approaches.},
keywords = {Bayes methods;crowdsourcing;Gaussian processes;RSSI;Gaussian processes;crowdsourcing;crowdsource-based signal strength field estimation;received signal strength;Bayesian approach;Loss measurement;Estimation;Kernel;Europe;Gaussian processes;Monitoring;Transmitters;Sensor networks;Bayesian estimation;regression;spectrum sensing;Gaussian processes},
doi = {10.23919/EUSIPCO.2017.8081401},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347490.pdf},
}Downloads: 0
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