Satellite navigation is very widespread in civil society; many devices and services exploit this technology and several systems are in use or in development phase. GNSS receiver, embedded in devices used in daily life (smartphones, cars and so on), works in several conditions and operational scenarios. Ensuring good positioning accuracy is challenging, especially in environment where receiver measurements are affected by gross errors, such as urban canyons. In this paper, the benefit of robust estimators in case of multiple simultaneous blunders is investigated; several robust estimators were implemented and their performances are compared with classical techniques used in GNSS context (Weighted Least Square, Receiver Autonomous Integrity Monitoring) using real data. Effectiveness of these methods raised from tests conducted in static and kinematic mode. Experimental results show significant enhancements for the proposed robust estimators, up to 45% for the horizontal RMS error and up to 52% for the vertical one.

Robust estimation methods applied to GPS in harsh environments

Angrisano A.
2017-01-01

Abstract

Satellite navigation is very widespread in civil society; many devices and services exploit this technology and several systems are in use or in development phase. GNSS receiver, embedded in devices used in daily life (smartphones, cars and so on), works in several conditions and operational scenarios. Ensuring good positioning accuracy is challenging, especially in environment where receiver measurements are affected by gross errors, such as urban canyons. In this paper, the benefit of robust estimators in case of multiple simultaneous blunders is investigated; several robust estimators were implemented and their performances are compared with classical techniques used in GNSS context (Weighted Least Square, Receiver Autonomous Integrity Monitoring) using real data. Effectiveness of these methods raised from tests conducted in static and kinematic mode. Experimental results show significant enhancements for the proposed robust estimators, up to 45% for the horizontal RMS error and up to 52% for the vertical one.
2017
978-1-5090-5922-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3184667
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