Robust estimation methods applied to GPS in harsh environments

Gaglione, Salvatore; Innac, Anna; Carbone, Sonia Pastore; Troisi, S.; Angrisano, Antonio

doi:10.1109/euronav.2017.7954169

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…WLS estimation is more susceptible to outliers compared with KF-based estimation. Robust regression, based on WLS residuals analysis, can adjust the weight measurements probably affected by multipath/NLOS reception whose corresponding residuals are large [18,19]. Such a process is a generalization of the maximum likelihood estimator (M-estimator), and is applied in this study.…”

Section: Robust Regression With Odometer Aidingmentioning

confidence: 99%

The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

Chiang

Hsu

et al. 2020

Remote Sensing

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Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications.

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Section: Robust Regression With Odometer Aidingmentioning

confidence: 99%

The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

Chiang

Hsu

et al. 2020

Remote Sensing

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“…The total integration time is related with the equivalent bandwidth

of the tracking loops as

. As usually considered in weighted-least square (WLS) algorithms [ 27 ], the quality of the GNSS signal can be modeled according to the satellite elevation as

where

is the reference

.…”

Section: Characterization Of the Observablesmentioning

confidence: 99%

Methodology for Simulating 5G and GNSS High-Accuracy Positioning

Peral-Rosado

Saloranta

Destino

et al. 2018

Sensors

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This paper focuses on the exploitation of fifth generation (5G) centimetre-wave (cmWave) and millimetre-wave (mmWave) transmissions for high-accuracy positioning, in order to complement the availability of Global Navigation Satellite Systems (GNSS) in harsh environments, such as urban canyons. Our goal is to present a representative methodology to simulate and assess their hybrid positioning capabilities over outdoor urban, suburban and rural scenarios. A novel scenario definition is proposed to integrate the network density of 5G deployments with the visibility masks of GNSS satellites, which helps to generate correlated scenarios of both technologies. Then, a generic and representative modeling of the 5G and GNSS observables is presented for snapshot positioning, which is suitable for standard protocols. The simulations results indicate that GNSS drives the achievable accuracy of its hybridisation with 5G cmWave, because non-line-of-sight (NLoS) conditions can limit the cmWave localization accuracy to around 20 m. The 5G performance is significantly improved with the use of mmWave positioning with dominant line-of-sight (LoS) conditions, which can even achieve sub-meter localization with one or more base stations. Therefore, these results show that NLoS conditions need to be weighted in 5G localization, in order to complement and outperform GNSS positioning over urban environments.

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“…In here, the classical least-square estimator, which is a MMSE (Minimum Mean Square Error) estimator is substituted by a different cost function. Prior work on the use of robust estimators for GNSS positioning can be found in the literature, for instance in [6,7,8]. Early publications showed however marginal improvement with respect to the LS.…”

Section: Introductionmentioning

confidence: 99%

Design and Evaluation of Robust M-estimators for GNSS Positioning in Urban Environments

Crespillo¹,

Andreetti²,

Grosch³

2020

The International Technical Meeting of the the Institute of Navigation

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Lausanne (EPFL) BIOGRAPHIES Omar García Crespillo holds a M.Sc. in Telecommunication Engineering from the University of Malaga in Spain. In 2013, he joined the Navigation department of the German Aerospace Center (DLR) where his current field of research includes multi-sensor fusion algorithms, GNSS, inertial sensors and integrity monitoring for safe ground and air transportation systems. Since 2015, he is also a PhD student at the Swiss Federal Institute of Technology (EPFL) in Lausanne. Alice Andreetti is currently a last year Master student in Environmental Engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL). She chose the specialization in environmental monitoring and modeling and she is currently working on the master thesis in the field of GNSS positioning. She received in 2017 the bachelor degree from the same university in Environmental Science and Engineering. In 2019 she stayed at the German Aerospace Center (DLR) for a 4 months internship.

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Robust estimation methods applied to GPS in harsh environments

Cited by 15 publications

References 16 publications

The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

Methodology for Simulating 5G and GNSS High-Accuracy Positioning

Design and Evaluation of Robust M-estimators for GNSS Positioning in Urban Environments

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