NLOS Signal Detection Based on Single Orthogonal Dual-Polarized GNSS Antenna

Zhang, Ke; Li, Baiyu; Chen, Huaming; Sun, Guangfu

doi:10.1155/2017/8548427

Cited by 8 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…Maqsood et al [23] proposed compact cross-plate reflection planes with a low multipath effect. Zhang et al [24] proposed a single orthogonal dual linearly polarized GNSS technology to suppress multipath effects through dual linearly polarized antennas. The multipath suppression method based on the antenna technology has a poor multipath suppression effect on low-altitude observation satellites.…”

Section: Multipath Error Mitigation Methodsmentioning

confidence: 99%

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

Zhang,

Sun

et al. 2024

Remote Sensing

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A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation.

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Section: Multipath Error Mitigation Methodsmentioning

confidence: 99%

GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods

Zhang,

Sun

et al. 2024

Remote Sensing

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“…We present two signal reception state, namely, LOS and Multipath reception. The LOS signal transmitted by satellites is right-hand circular polarized (RHCP) [22]. When the signal is reflected, with an odd reflection number, the received signal become left-hand circular polarized (LHCP) [23].…”

Section: Proposed Svm-based Classifier Descriptionmentioning

confidence: 99%

Support Vector Machines for Improving Vehicle Localization in Urban Canyons

2018

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Since the middle ages, the need to identify the vehicles position in their local environment has always been a necessity and a challenge. Today, GNSS-based positioning systems have penetrated several field, such as land transport, emergency systems and civil aviation requiring high positioning accuracy. However, the performances of GNSS-based systems can be degraded in harsh environment due to non-line-of-sight (NLOS), Multipath and masking effects. In this paper, for improving vehicle localization in urban canyons, we address a very challenging problem related to GNSS signal reception state detection (LOS, NLOS or Multipath). A SVMbased system for GNSS Multipath detection using the fusion of information provided by two GNSS antennas is proposed. In this work, we aim to explore the potential of machine learning, and more precisely, Support Vector Machines (SVM) to identify GNSS signals reception state. The SVM-based system developed in this work has used the C/N0 of signals provided by RHCP and LHCP antennas, and satellite elevation as classification criteria. The training data set is constructed by several experimental studies done in real environments, Calais, France . Furthermore, four SVM kernel functions are tested, namely, Linear, Gaussian, Cubic and Quadratic. A GNSS signal reception state detection by applying the proposed SVM-based classifier is demonstrated on real GPS signals, and the efficiency of the system is shown. We obtain empirically an accuracy of signal detection about 93%.

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