Filtering GPS time-series using a Vondrak filter and cross-validation

Zheng, Da Wei; Zhong, Ping; Ding, Xiaoli; Chen, Wu

doi:10.1007/s00190-005-0474-x

Cited by 93 publications

(52 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the point of measurement, the use of choke ring antennas or special architecture receivers with built-in multipath mitigation techniques eliminates much of the code multipath, leaving carrier phase multipath still dominant, which is a more embedded and harder-to-mitigate source of error (Filippov et al 1999;Van Dierendonck and Braaasch 1997). Other recent techniques reduce the multipath error at the post-processing stage, minimizing carrier phase or code multipath using either wavelet analysis (Satirapod and Rizos 2005), a Vondrak filter with cross-validation (Zheng et al 2005), or weighting the data using the observed signal to noise ratio (Lau and Mok 1999). Satirapod and Rizos (2005) applied wavelet decomposition to GPS double difference residuals in order to extract GPS carrier phase multipath.…”

Section: Introductionmentioning

confidence: 99%

“…By using filters of different cut-off frequencies and comparison with zero baseline double difference residuals, the optimal level of wavelet filtering is deduced. In contrast, Zheng et al (2005) separated carrier phase signals from noise whenever the noise level is lower than the magnitude of the signal, using a Vondrak filter based on different smoothing factors, with cross validation to determine the optimal smoothing factor. This combination gives a balance between data fitting and smoothing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GPS sidereal filtering: coordinate- and carrier-phase-level strategies

Ragheb

Clarke

Edwards

2006

J Geod

109

View full text Add to dashboard Cite

Multipath error is considered one of the major errors affecting GPS observations. One can benefit from the repetition of satellite geometry approximately every sidereal day, and apply filtering to help minimize this error. For GPS data at 1 second interval processed using a double-difference strategy, using the day-to-day coordinate or phase residual autocorrelation determined with a 10 hour window leads to the steadiest estimates of the error-repeat lag, although a window as short as 2 hours can produce an acceptable value with >97% of the optimal lag's correlation. We conclude that although the lag may vary with time, such variation is marginal and there is little advantage in using a satellite-specific or other time-varying lag in double-difference processing.We filter the GPS data either by stacking a number of days of processed coordinate residuals using the optimum "sidereal" lag (23h 55m 54s), and removing these This is the authors' version of a manuscript that has been accepted by Journal of Geodesy. The final publication is available at Springer via doi:10.1007/s00190-006-0113-1 2 stacked residuals from the day in question (coordinate space), or by a similar method using double-difference phase residuals (observational space). Either method results in more consistent and homogeneous set of coordinates throughout the dataset compared with unfiltered processing. Coordinate stacking reduces geometry-related repeating errors (mainly multipath) better than phase residual stacking, although the latter takes less processing time to achieve final filtered coordinates. Thus the optimal stacking method will depend on whether coordinate precision or computational time is the over-riding criterion.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GPS sidereal filtering: coordinate- and carrier-phase-level strategies

Ragheb

Clarke

Edwards

2006

J Geod

109

View full text Add to dashboard Cite

show abstract

“…where ∆η(t i ) is the sea level anomaly, ∆η(t i ) is the smoothed sea level anomaly at t i using the Vondrak filter and cross-validation [37].…”

Section: A Priori Constraintmentioning

confidence: 99%

Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data

Zhang

Jin

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The Gravity Recovery and Climate Experiment (GRACE) mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C 20 of GRACE is poor, and no GRACE data are available before 2002 and there will likely be a gap between the GRACE and GRACE-FOLLOW-ON mission. To compensate for GRACE's shortcomings, in this paper, we provide an alternative way to invert Earth's time-varying gravity field, using a priori degree variance as a constraint on amplitudes of Stoke's coefficients up to degree and order 60, by combining continuous GPS coordinate time series and satellite altimetry (SA) mean sea level anomaly data from January 2003 to December 2012. Analysis results show that our estimated zonal low-degree gravity coefficients agree well with those of GRACE, and large-scale mass distributions are also investigated and assessed. It was clear that our method effectively detected global large-scale mass changes, which is consistent with GRACE observations and the GLDAS model, revealing the minimums of annual water cycle in the Amazon in September and October. The global mean mass uncertainty of our solution is about two times larger than that of GRACE after applying a Gaussian spatial filter with a half wavelength at 500 km. The sensitivity analysis further shows that ground GPS observations dominate the lower-degree coefficients but fail to contribute to the higher-degree coefficients, while SA plays a complementary role at higher-degree coefficients. Consequently, a comparison in both the spherical harmonic and geographic domain confirms our global inversion for the time-varying gravity field from GPS and Satellite Altimetry.

show abstract

“…Vondrak filter method is an effective smoothing method that aims to compromise between two extreme circumstance, namely absolute smoothing (S) and absolute fitting (F), and it does not require any predefined fitting function, the objective function of Vondrak filter can be expressed as follows [20,34] =1/   ,should be carefully selected, it is obviously stronger smoothing effect can be obtained with smaller  ,the frequency response method was applied to determine the optimal smoothing factor in this paper. In fact, Vondrak filter is applicable to extract both short-term and long-term components from original measurements when using an optimal smoothing factor.…”

Section: 1vondrak Filtermentioning

confidence: 99%

“…Many multipath mitigation strategies have been proposed in recent years, including the hardware-based methods [16,17] and software-based methods [18,19]. It has been found that the sidereal filtering is an easy method that can be used to remove the multipath effects [20,21]. However, in real deformation monitoring applications, dynamic multipath effects change with time due to the changing of the geometry of satellites and reflective surfaces [22].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

Han

Wang

Meng

et al. 2016

Engineering Structures

View full text Add to dashboard Cite

(2016) Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading. Engineering Structures, 122 (2016 A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading AbstractLarge flexible engineering structures, such as long span bridges or tall buildings, are susceptible to quasi-static and dynamic deformations caused by different loading situations, thus accurate displacement measurements are desirable to assess the integrity and reliability of the structure. In this study, an integrated system that includes Global Positioning System (GPS), accelerometer and anemometer was developed to obtain the responses of a long span bridge to the extreme wind loading. Spectral analysis based on the Fast Fourier Transform (FFT) algorithm was first carried out to detect the dominant frequencies of the bridge. Then the noisy GPS displacement measurements and accelerometer data are de-noised using the Vondrak filter, and the low frequency disturbance was separated from GPS displacement time series. A least-squares based displacement reconstruction scheme using noise-mitigated accelerations was employed, and the Tikhonov regularization scheme with optimal selected regularization factor was used to alleviate the ill-posedness. At last, an adaptive recursive least squares (RLS) filter was adopted to separate the slow-varying movements, and the total displacement with enhanced measurement accuracy was obtained from the combined quasistatic and high-frequency dynamic displacements. A field monitoring data set collected on the Erqi Yangtze River Bridge, a three-tower cable-stayed bridge located in Wuhan in China, was used to validate the effectiveness of the proposed integration processing scheme. The GPS/accelerometer/anemometer installed on the center supporting tower was used to characterize the interaction between the responses and the ambient wind loadings. The results demonstrate the proposed technique can significantly improve the measurement accuracy of bridge displacement under strong winds. The deformation with the amplitude of several millimeters can be successfully detected, and the spectrum of the bridge response obtained from both GPS data and accelerometer data reveals the identified first natural frequency of the bridge is 0.172 Hz.

show abstract

Filtering GPS time-series using a Vondrak filter and cross-validation

Cited by 93 publications

References 11 publications

GPS sidereal filtering: coordinate- and carrier-phase-level strategies

GPS sidereal filtering: coordinate- and carrier-phase-level strategies

Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data

Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

Contact Info

Product

Resources

About