A sequential estimation approach to terrestrial reference frame determination

Abstract We review the main concepts underlying the determination of terrestrial reference frames (TRFs) through a recursive algorithm based on Kalman Filtering and Rauch-Tung-Striebel (RTS) smoothing which is currently adopted at Jet Propulsion Laboratory (JPL) to compute sub-secular frame products (JTRFs). We contextualize the TRF determination in the state-space framework and we emphasize connections between frame state, its observability through space-geodetic frame inputs and the similarity transformation which is central to frame definition. We elaborate on the notion of sub-secular frame, enabled by our approach, in constrast to standard TRF products which, secular by construction, are designed to represent the long-term mean physical properties of the frame. Comparisons of JTRF solutions to standard products such as the International Terrestrial Reference Frame (ITRF) suggest high-level consistency in a long-term sense with time derivatives of the Helmert transformation parameters connecting the two TRFs below 0.18 mm/yr. We discuss advantages and limitations of JPL approach to TRF determination and outline lines of inquiries that are currently being researched as part of JTRF development plan.

[1]  Chung-Yen Kuo,et al.  Geodetic Observations and Global Reference Frame Contributions to Understanding Sea‐Level Rise and Variability , 2010 .

[2]  Horst Müller,et al.  Evaluation of DTRF2014, ITRF2014, and JTRF2014 by Precise Orbit Determination of SLR Satellites , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Pascal Willis,et al.  The International DORIS Service contribution to the 2014 realization of the International Terrestrial Reference Frame , 2016 .

[4]  Z. Altamimi,et al.  ITRF2014: A new release of the International Terrestrial Reference Frame modeling nonlinear station motions , 2016 .

[5]  M. Heflin,et al.  Stacking global GPS verticals and horizontals to solve for the fortnightly and monthly body tides: Implications for mantle anelasticity , 2015 .

[6]  Manuela Seitz,et al.  The 2008 DGFI realization of the ITRS: DTRF2008 , 2012, Journal of Geodesy.

[7]  Robert W. King,et al.  Estimating regional deformation from a combination of space and terrestrial geodetic data , 1998 .

[8]  J. Zumberge,et al.  Precise point positioning for the efficient and robust analysis of GPS data from large networks , 1997 .

[9]  Nikita P. Zelensky,et al.  Impact of ITRS 2014 realizations on altimeter satellite precise orbit determination , 2018 .

[10]  M. Tamisiea,et al.  On seasonal signals in geodetic time series , 2012 .

[11]  Z. Altamimi,et al.  ITRF2008: an improved solution of the international terrestrial reference frame , 2011 .

[12]  Claude Boucher,et al.  A review of algebraic constraints in terrestrial reference frame datum definition , 2001 .

[13]  Manuela Seitz,et al.  Consistent realization of Celestial and Terrestrial Reference Frames , 2018, Journal of Geodesy.

[14]  M. Pearlman,et al.  Laser geodetic satellites: a high-accuracy scientific tool , 2019, Journal of Geodesy.

[15]  Z. Altamimi,et al.  Assessment of the accuracy of global geodetic satellite laser ranging observations and estimated impact on ITRF scale: estimation of systematic errors in LAGEOS observations 1993–2014 , 2016, Journal of Geodesy.

[16]  Thorne Lay,et al.  A review of the rupture characteristics of the 2011 Tohoku-oki Mw 9.1 earthquake , 2017 .

[17]  G. Blewitt,et al.  Harnessing the GPS Data Explosion for Interdisciplinary Science , 2018, Eos.

[18]  Zuheir Altamimi,et al.  ITRF2000: A new release of the International Terrestrial Reference Frame for earth science applications , 2002 .

[19]  Z. Altamimi,et al.  ITRF2005 : A new release of the International Terrestrial Reference Frame based on time series of station positions and Earth Orientation Parameters , 2007 .

[20]  D. Kuang,et al.  DORIS Satellite Phase Center Determination and Consequences on the Derived Scale of the Terrestrial Reference Frame , 2007 .

[21]  Yuji Yagi,et al.  A unified source model for the 2011 Tohoku earthquake , 2011 .

[22]  Stephen M. Lichten,et al.  Strategies for high-precision Global Positioning System orbit determination , 1987 .

[23]  Y. Bock,et al.  Anatomy of apparent seasonal variations from GPS‐derived site position time series , 2001 .

[24]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[25]  T. M. Chin,et al.  KALREF—A Kalman filter and time series approach to the International Terrestrial Reference Frame realization , 2015 .

[26]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[27]  P. Malanotte‐Rizzoli,et al.  An approximate Kaiman filter for ocean data assimilation: An example with an idealized Gulf Stream model , 1995 .

[28]  T. M. Chin,et al.  On Kalman filter solution of space-time interpolation , 2001, IEEE Trans. Image Process..

[29]  Xavier Collilieux,et al.  Comparison of very long baseline interferometry, GPS, and satellite laser ranging height residuals from ITRF2005 using spectral and correlation methods , 2007 .

[30]  R. Kopp,et al.  Estimating the sources of global sea level rise with data assimilation techniques , 2012, Proceedings of the National Academy of Sciences.

[31]  F. N. Teferle,et al.  External Evaluation of the Terrestrial Reference Frame: Report of the Task Force of the IAG Sub-commission 1.2 , 2014 .

[32]  Xavier Collilieux,et al.  Impact of loading effects on determination of the International Terrestrial Reference Frame , 2010 .

[33]  J. Ray,et al.  Anomalous harmonics in the spectra of GPS position estimates , 2008 .

[34]  M. Zhong,et al.  Contributions of thermal expansion of monuments and nearby bedrock to observed GPS height changes , 2009 .

[35]  J. Ray,et al.  The IGS contribution to ITRF2014 , 2016, Journal of Geodesy.

[36]  Pascal Willis,et al.  Terrestrial reference frame requirements within GGOS perspective , 2005 .

[37]  Zuheir Altamimi,et al.  Review of Reference Frame Representations for a Deformable Earth , 2019, IX Hotine-Marussi Symposium on Mathematical Geodesy.

[38]  M. Watkins,et al.  The gravity recovery and climate experiment: Mission overview and early results , 2004 .

[39]  D. Thaller,et al.  IVS contribution to ITRF2014 , 2016, Journal of Geodesy.

[40]  Richard S. Gross,et al.  A Kalman-filter-based approach to combining independent Earth-orientation series , 1998 .

[41]  Zuheir Altamimi,et al.  Long-term stability of the terrestrial reference frame , 2004 .

[42]  R. König,et al.  A new high-resolution model of non-tidal atmosphere and ocean mass variability for de-aliasing of satellite gravity observations: AOD1B RL06 , 2017 .

[43]  Na Wei,et al.  Contributions of thermoelastic deformation to seasonal variations in GPS station position , 2017, GPS Solutions.

[44]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[45]  C. Tourain,et al.  Initiating an error budget of the DORIS ground antenna position: Genesis of the Starec antenna type C , 2016 .

[46]  Z. Altamimi,et al.  The impact of a No‐Net‐Rotation Condition on ITRF2000 , 2003 .

[47]  S. Melachroinos,et al.  The effect of geocenter motion on Jason-2 orbits and the mean sea level , 2013 .

[48]  G. Moreaux,et al.  DORIS Starec ground antenna characterization and impact on positioning , 2016 .

[49]  Michael B. Heflin,et al.  JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System , 2017 .

[50]  T. M. Chin,et al.  Modeling and forecast of the polar motion excitation functions for short-term polar motion prediction , 2004 .

[51]  Xavier Collilieux,et al.  Accuracy of the International Terrestrial Reference Frame origin and Earth expansion , 2011 .

[52]  J. Ray,et al.  Measurements of length of day using the Global Positioning System , 1996 .

[53]  Bruce J. Haines,et al.  Towards the 1 mm/y Stability of the Radial Orbit Error at Regional Scales , 2014 .

[54]  Claudio Abbondanza,et al.  Height bias and scale effect induced by antenna gravitational deformations in geodetic VLBI data analysis , 2011 .

[55]  D. Simon Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[56]  James L. Davis,et al.  Geodesy by radio interferometry: The application of Kalman Filtering to the analysis of very long baseline interferometry data , 1990 .