@conference {156,
	title = {Mitigation of leverage observation effects in GNSS robust positioning},
	booktitle = {2018 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters, MetroSea 2018 - Proceedings},
	year = {2019},
	publisher = {Institute of Electrical and Electronics Engineers Inc.},
	organization = {Institute of Electrical and Electronics Engineers Inc.},
	abstract = {<p>Nowadays GNSSs are the most commonly used systems for localization; they are able to provide user absolute position with metric accuracy in benign environment, i.e. in scenarios without significant obstacles surrounding the user. On the other hand, in harsh scenarios GNSS performance are degraded, owing to the shortage of available measurements and/or to the presence of blunders among them. The blunder issue is usually addressed through RAIM techniques or robust estimation; the latter approach demonstrates often better performance, but suffers anyway the cases of multiple blunders and low redundancy. M-estimators, a particular class of robust estimators, are based on the minimization of functions of least squares residuals. A possible way to strengthen a M-estimator is to take into account for leverage observations, defined as measurements with high potential to affect estimation results. In this work, the Huber M-estimator is adapted to include information about leverage observations and is used to process GPS measurements, collected in harsh environment. The obtained results are very promising, with position errors reduction even beyond 50\% with respect to classical Huber method. {\textcopyright} 2018 IEEE.</p>},
	doi = {10.1109/MetroSea.2018.8657870},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063866479\&doi=10.1109\%2fMetroSea.2018.8657870\&partnerID=40\&md5=05cadc7af731b0d1dffe0c904d6ac49c},
	author = {Angrisano, A. and Gaglione, S.}
}
