Noise in two‐color electronic distance meter measurements revisited

By John Langbein. 2004

200 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Civil and Structural Engineering, Geophysics

View Paper: https://www.semanticscholar.org/paper/74287d24b68b1280946e1ad4134281de47097df8

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Citations in the corpus

A continuous GPS coordinate time series analysis strategy for high-accuracy vertical land movements (2008, background): “…research on the effect of time-correlated (coloured) noise (e.g. Langbein & Johnson, 1997; Zhang et al., 1997; Mao et al., 1999; Williams, 2003a; Langbein, 2004; Williams et al., 2004), the reduction of spatially correlated variations (e.g. Wdowinski et al., 1997; Nikolaidis, 2002; Johansson et…”
CATS: GPS coordinate time series analysis software (2008, methodology): “When it became apparent that the routines were becoming too slow and there was a need for a package that is independent of commercial software, the routines were consolidated and re-written in C. The background to this method, in the context of geodetic observations, can be found in the following pa”
Network-based estimation of time-dependent noise in GPS position time series (2015, methodology): “The more commonly used method is maximum likelihood estimation (MLE) applied to individual component time series (e.g., Langbein 2004, Williams et al. 2004).”
Fast error analysis of continuous GNSS observations with missing data (2013, methodology): “Equations (1) to (3), with the before mentioned procedure of dealing with missing data, are implemented in the CATS software (Williams 2008) and in the program of Langbein (2010) and we will call this the standard method.”
On the nature of GPS draconitic year periodic pattern in multivariate position time series (2013, methodology): “For the properties of the vector operator and the Kronecker product, we refer to Magnus [1988]. The m r matrix Y= [y1 y2.”
Correlated errors in GPS position time series: Implications for velocity estimates (2011, methodology): “[41] Therefore to properly account for the colored noise dependency on data time period, the known data covariance matrix used in noise analyses [e.g., Langbein and Johnson, 1997; Zhang et al., 1997; Mao et al., 1999; Langbein, 2004; Williams, 2003b; Williams et al., 2004; Amiri‐Simkooei et al., 200”
Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis (2013, background): “…(Shen and Li 2011), the maximum likelihood estimation (Zhang et al. 1997; Langbein and Johnson 1997; Mao et al. 1999; Williams 2003; Williams et al. 2004; Langbein 2004; Williams and Willis 2006) or the least squares variance component estimation (Amiri-Simkooei et al. 2007; Amiri-Simkooei 2009).”
Non-negative least-squares variance component estimation with application to GPS time series (2016, methodology): “The VCEmethods based on the maximum likelihood estimation (MLE) principle have been applied to GPS position time series for which we refer to Zhang et al. (1997), Mao et al. (1999), Williams et al. (2004), Langbein (2004).”
Noise in multivariate GPS position time-series (2009, methodology): “One can also use the Hosking flicker noise covariance matrix,whichwasintroduced and usedbyWilliams(2003a), Langbein (2004) , Williams et al. (2004), Beavan (2005) and Bos et al. (2008).”
Uncertainty of absolute gravity measurements (2005, unknown): “This argument was also put forward to reject some noise models for geodetic data by Langbein [2004] .”