Effects on noise properties of GPS time series caused by higher-order ionospheric corrections

Jiang, Weiping; Deng, Liansheng; Li, Zhao; Zhou, Xiaohui; Liu, Hongfei

doi:10.1016/j.asr.2013.12.037

Cited by 26 publications

(14 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, monument noise in the GNSS position time series is always believed to be a random walk process and the deep-drilled braced monument seems to have the least amplitude of random walk noise [9]. Jiang et al indicated that, with the higher-order ionospheric (HOI) correction considered, white noise amplitude in the up direction decreased by 81.8%, while flicker noise amplitudes in the north direction decreased by 67.5% [18].…”

Section: Introductionmentioning

confidence: 99%

Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

et al. 2018

View full text Add to dashboard Cite

The noise characteristics of the Global Navigation Satellite System (GNSS) position time series can be biased by many factors, which in turn affect the estimates of parameters in the deterministic model using a least squares method. The authors assess the effects of seasonal signals, weight matrix, intermittent offsets, and Helmert transformation parameters on the noise analyses. Different solutions are obtained using the simulated and real position time series of 647 global stations and power law noise derived from the residuals of stacking solutions are compared. Since the true noise in the position time series is not available except for the simulated data, the authors paid most attention to the noise difference caused by the variable factors. First, parameterization of seasonal signals in the time series can reduce the colored noise and cause the spectral indexes to be closer to zero (much “whiter”). Meanwhile, the additional offset parameters can also change the colored noise to be much “whiter” and more offsets parameters in the deterministic model leading to spectral indexes closer to zero. Second, the weight matrices derived from the covariance information can induce more colored noise than the unit weight matrix for both real and simulated data, and larger biases of annual amplitude of simulated data are attributed to the covariance information. Third, the Helmert transformation parameters (three translation, three rotation, and one scale) considered in the model show the largest impacts on the power law noise (medians of 0.4 mm−k/4 and 0.06 for the amplitude and spectral index, respectively). Finally, the transformation parameters and full-weight matrix used together in the stacking model can induce different patterns for the horizontal and vertical components, respectively, which are related to different dominant factors.

show abstract

Section: Introductionmentioning

confidence: 99%

Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

et al. 2018

View full text Add to dashboard Cite

show abstract

“…wrms WRMS wrms (13) where ΔWRM S neu is the WRMS variation rate in the North, East and Up component respectively, Δwrms neu denote the WRMS difference in the three components, which are shown in the left panels of Figure 4. wrms neu origina l represents the WRMS of each of the three components without HOI delay correction.…”

Section: Impact On the Scatter Of The Gps Coordinate Time Seriesmentioning

confidence: 99%

“…At GPS frequencies, the fourth order and subsequent terms are negligible [32]. In recent years, higher-order ionospheric (HOI) effects, as one type of GPS technique-specific errors, have been studied on the GPS coordinates by many authors [3,7,8,11,13,18,26]. Munekane analysed the influence of the second order ionospheric delay model [26].…”

Section: Introductionmentioning

confidence: 99%

High-order Ionospheric Effects on GPS Coordinate Time Series

Jiang¹,

Deng²,

Li³

2015

Satellite Positioning - Methods, Models and Applications

Self Cite

View full text Add to dashboard Cite

“…Some research has shown that monument instability leads to random walk behavior (Wyatt 1989). The origin of flicker noise is less clear, but time-variable satellite geometry, long-term orbit mismodeling, and multipath have been suggested as possible contributors (King and Watson 2010;Ray et al 2008;Jiang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Network-based estimation of time-dependent noise in GPS position time series

Dmitrieva

Segall

DeMets

2015

J Geod

View full text Add to dashboard Cite

Some estimates of GPS velocity uncertainties are very low, <0.1 mm/year with 10 years of data. Yet, residual velocities relative to rigid plate models in nominally stable plate interiors can be an order of magnitude larger. This discrepancy could be caused by underestimating low-frequency time-dependent noise in position time series, such as random walk. We show that traditional estimators, based on individual time series, are insensitive to low-amplitude random walk, yet such noise significantly increases GPS velocity uncertainties. Here, we develop a method for determining representative noise parameters in GPS position time series, by analyzing an entire network simultaneously, which we refer to as the network noise estimator (NNE). We analyze data from the aseismic central-eastern USA, assuming that residual motions relative to North America, corrected for glacial isostatic adjustment (GIA), represent noise. The position time series are decomposed into signal (plate rotation and GIA) and noise components. NNE simultaneously processes multiple stations with a Kalman filter and solves for average noise components for the network by maximum likelihood estimation. Synthetic tests show that NNE correctly estimates even low-level random walk, thus providing better estimates of velocity uncertainties than conventional, single station methods. To test NNE on actual data, we analyze a heterogeneous 15 station GPS network from the centraleastern USA, assuming the noise is a sum of random walk, flicker and white noise. For the horizontal time series, NNE finds higher average random walk than the standard individual station-based method, leading to velocity uncertainties a factor of 2 higher than traditional methods.

show abstract

Effects on noise properties of GPS time series caused by higher-order ionospheric corrections

Cited by 26 publications

References 33 publications

Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

Impacts on Noise Analyses of GNSS Position Time Series Caused by Seasonal Signal, Weight Matrix, Offset, and Helmert Transformation Parameters

High-order Ionospheric Effects on GPS Coordinate Time Series

Network-based estimation of time-dependent noise in GPS position time series

Contact Info

Product

Resources

About