GNSS Multipath Detection Using Continuous Time-Series C/N0

Kubo, Nobuaki; Kobayashi, Kaito; Furukawa, Ryo

doi:10.3390/s20144059

Cited by 32 publications

(15 citation statements)

References 13 publications

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“…BSs with censored PL are assumed to be in NLOS. In practice, the LOS state needs to be detected [ 41 , 42 , 43 ]. One 28 GHz case is presented with 100%, 95%, 90%, 85%, and 80% LOS detection rates in the positioning phase.…”

Section: Positioning Simulation Resultsmentioning

confidence: 99%

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Karttunen

Valkama

Talvitie

2021

Sensors

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Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

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Section: Positioning Simulation Resultsmentioning

confidence: 99%

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Karttunen

Valkama

Talvitie

2021

Sensors

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“…Among all the measurements that GNSS receivers provide, they may all suffer from the presence of jamming. Interference detection can be implemented by exploiting carrier-to-noise density (

, also known as CNR) measurements [ 38 , 39 ], as the presence of a jammer can be suspected by a significant drop in the CNR values [ 40 , 41 ], resulting in code and/or phase observations not being recorded. The attenuation of the jamming quality can be observed indirectly by comparing the responses of devices of different quality to jamming [ 26 , 27 ].…”

Section: Methodsmentioning

confidence: 99%

Robustness against Chirp Signal Interference of On-Board Vehicle Geodetic and Low-Cost GNSS Receivers

Dimc

Pavlovčič-Prešeren

Bažec

2021

Sensors

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Robust autonomous driving, as long as it relies on satellite-based positioning, requires carrier-phase-based algorithms, among other types of data sources, to obtain precise and true positions, which is also primarily true for the use of GNSS geodetic receivers, but also increasingly true for mass-market devices. The experiment was conducted under line-of-sight conditions on a straight road during a period of no traffic. The receivers were positioned on the roof of a car travelling at low speed in the presence of a static jammer, while kinematic relative positioning was performed with the static reference base receiver. Interference mitigation techniques in the GNSS receivers used, which were unknown to the authors, were compared using (a) the observed carrier-to-noise power spectral density ratio as an indication of the receivers’ ability to improve signal quality, and (b) the post-processed position solutions based on RINEX-formatted data. The observed carrier-to-noise density generally exerts the expected dependencies and leaves space for comparisons of applied processing abilities in the receivers, while conclusions on the output data results comparison are limited due to the non-synchronized clocks of the receivers. According to our current and previous results, none of the GNSS receivers used in the experiments employs an effective type of complete mitigation technique adapted to the chirp jammer.

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“…However, GNSS receivers observe both LOS signals and non-line-of-sight (NLOS) signals in these areas. NLOS signals have large multipath errors, which is the reason for the degrading positioning accuracy in dense urban areas ( Kubo et al, 2020 ). Under kinematic conditions, the environment around the GNSS antenna is dramatically changed and the GNSS receiver is less likely to track NLOS signals, but under static conditions, the environment around the GNSS antenna is unchanged and more NLOS signals can be tracked by the GNSS receiver ( Kubo et al (2017) .…”

Section: Introductionmentioning

confidence: 99%

GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check

Ozeki

Kubo

2022

Front. Robot. AI

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Global navigation satellite system (GNSS) positioning has recently garnered attention for autonomous driving, machine control, and construction sites. With the development of low-cost multi-GNSS receivers and the advent of new types of GNSS, such as Japan’s Quasi-Zenith Satellite System, the potential of GNSS positioning has increased. New types of GNSS directly increase the number of line-of-sight (LOS) signals in dense urban areas and improve positioning accuracy. However, GNSS receivers can observe both LOS and non-line-of-sight (NLOS) signals in dense urban areas, and more NLOS signals are observed under static conditions than under dynamic conditions. The classification of LOS and NLOS signals is important, and various methods have been proposed, such as C/N0, using three-dimensional maps, fish-eye view, and GNSS/inertial navigation system integration. Multipath detection based on machine learning has also been reported in recent years. In this study, we propose a method for detecting NLOS signals using a support vector machine (SVM) classifier modeled with unique features that are calculated by receiver independent exchange format-based information and GNSS pseudorange residual check. We found that using both the SVM classifier and GNSS pseudorange residual check effectively reduced the error due to NLOS signals. Several static tests were conducted near high-rise buildings that are likely to receive some NLOS signals in downtown Tokyo. For all static tests, the percentage of positioning errors within 10 m in the horizontal positioning error was improved by >80% by detecting and eliminating satellites receiving NLOS signals.

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GNSS Multipath Detection Using Continuous Time-Series C/N0

Cited by 32 publications

References 13 publications

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning

Robustness against Chirp Signal Interference of On-Board Vehicle Geodetic and Low-Cost GNSS Receivers

GNSS NLOS Signal Classification Based on Machine Learning and Pseudorange Residual Check

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