goGPS: open source software for enhancing the accuracy of low-cost receivers by single-frequency relative kinematic positioning

Realini, Eugenio; Reguzzoni, Mirko

doi:10.1088/0957-0233/24/11/115010

Cited by 86 publications

(68 citation statements)

References 14 publications

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“…Figure 3 shows the position of the two radiosonde stations used for validation of GNSS-derived PWV. (Realini and Reguzzoni 2013;Herrera et al 2016), modified by the authors to perform precise point positioning (PPP) (Kouba and Héroux 2001) and estimate ZTD values. The most recent version of the software, including the PPP module and ZTD estimation capability, is freely downloadable from the goGPS project GitHub page (https://github.com/goGPS-Project).…”

Section: Experimental Setup and Data Processingmentioning

confidence: 99%

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Barindelli

Realini²,

Venuti

et al. 2018

Earth Planets Space

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GNSS atmospheric water vapor monitoringis not yet routinely performed in Italy, particularly at the regional scale. However, in order to support the activities of regional environmental protection agencies, there is a widespread need to improve forecasting of heavy rainfall events. Localized convective rain forecasts are often misplaced in space and/ or time, causing inefficiencies in risk mitigation activities. Water vapor information can be used to improve these forecasts. In collaboration with the environmental protection agencies of the Lombardy and Piedmont regions in northern Italy, we have collected and processed GNSS and weather station datasets for two heavy rain events: one which was spatially widespread, and another which was limited to few square kilometers. The time variations in water vapor derived from a regional GNSS network with inter-station distances on the order of 50 km were analyzed, and the relationship between the time variations and the evolution of the rain events was evaluated. Results showed a signature associated with the passage of the widespread rain front over each GNSS station within the area of interest. There was a peak in the precipitable water vapor value when the heavier precipitation area surrounded the station, followed by a steep decrease (5-10 mm in about 1 h) as the rainclouds moved past the station. The smaller-scale event, a convective storm a few kilometers in extent, was not detected by the regional GNSS network, but strong fluctuations in water vapor were detected by a low-cost station located near the area of interest.

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Section: Experimental Setup and Data Processingmentioning

confidence: 99%

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Barindelli

Realini²,

Venuti

et al. 2018

Earth Planets Space

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“…, 2s k by changing (a 1,k , c 1,k ) to (a 2,k , c 2,k ). Note that the hyperparameters a 1,k and a 2,k control the amplitudes of the non-zero MP components in the pseudoranges and pseudorange rates and that w i,k is a weight defined as an increasing function of (C/N0) i,k (as in [8]) and of the ith satellite elevation. Indeed, the higher (C/N0) i,k , the better.…”

Section: Mp Vector M Kmentioning

confidence: 99%

“…where the likelihood f (y k |θk) has been defined in (8), the parameter prior f (θ k , z k |ϕ k ) in (18) and the hyperprior f (ϕ k ) in (21).…”

Section: Posterior Distributionmentioning

confidence: 99%

“…Fixed biases were then generated between time instants 50 and 130 in 3 channels (corresponding to 3 satellites). Noise variances were finally generated in agreement with the values of C/N0 as in (7) and the weights w i,k appearing in the MP prior were computed following [8] (with the corresponding C/N0 values). More details about the simulation scenario including the trajectory and the parameters of interest can be found in [4].…”

Section: Experimental Validationmentioning

confidence: 99%

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Multipath Mitigation in Global Navigation Satellite Systems Using a Bayesian Hierarchical Model With Bernoulli Laplacian Priors

Lesouple

Tourneret

Sahmoudi

et al. 2018

2018 IEEE Statistical Signal Processing Workshop (SSP)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. ABSTRACTA new sparse estimation method was recently introduced in a previous work to correct biases due to multipath (MP) in GNSS measurements. The proposed strategy was based on the resolution of a LASSO problem constructed from the navigation equations using the reweighted-1 method. This strategy requires to adjust the regularization parameters balancing the data fidelity term and the involved regularizations. This paper introduces a new Bayesian estimation method allowing the MP biases and the unknown model parameters and hyperparameters to be estimated directly from the GNSS measurements. The proposed method is based on BernoulliLaplacian priors, promoting sparsity of MP biases.

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“…(30), the bounds of the specific acceleration of the vehicle can be represented using the vehicle acceleration and gravity as follows. We assume that the vehicle accelerates at a m/sec 2 .…”

Section: Relative Position Error Boundingmentioning

confidence: 99%

Optimal Selection of an Inertial Sensor for Cycle Slip Detection Considering Single-frequency RTK/INS Integrated Navigation

Kim

Song

et al. 2016

Trans. Japan Soc. Aero. S Sci.

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This paper suggests a methodology for the selection of an inertial sensor for cycle slip detection. The satellitedifference and time-difference residual between the predicted and measured carrier phases is defined as the monitoring value for cycle slip detection. The inertial navigation system (INS) position is used to calculate the predicted observation, and its error mainly contributes to the residual. For one cycle slip detection, the monitoring value residual should be smaller than the threshold. Based on this approach, we derive the maximum permissible position estimation error of the INS to detect one cycle slip. An Earth-centered, Earth-fixed (ECEF) frame-based INS is used for the derivations. Then, we formulate the position estimation error as a function of the inertial sensor specifications using suitable assumptions. Using resulting equation, the optimal inertial sensor can be selected based on the required cycle slip detection performance. To verify the process of optimal IMU selection, simulation data is used. The derived position estimation error of the INS is analyzed carefully, and the monitoring value elicited by the INS error is also investigated. As a result, the selected IMU satisfies the designed cycle slip detection performance within the microelectromechanical systems (MEMS) grade.

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goGPS: open source software for enhancing the accuracy of low-cost receivers by single-frequency relative kinematic positioning

Cited by 86 publications

References 14 publications

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Detection of water vapor time variations associated with heavy rain in northern Italy by geodetic and low-cost GNSS receivers

Multipath Mitigation in Global Navigation Satellite Systems Using a Bayesian Hierarchical Model With Bernoulli Laplacian Priors

Optimal Selection of an Inertial Sensor for Cycle Slip Detection Considering Single-frequency RTK/INS Integrated Navigation

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