Improvement of Reflection Detection Success Rate of GNSS RO Measurements Using Artificial Neural Network

Global Navigation Satellite System (GNSS) radio occultation (RO) has been widely used in the prediction of weather, climate, and space weather, particularly in the area of tropospheric analyses. However, one of the issues with GNSS RO measurements is that they are interfered with by the signals reflected from the earth’s surface. Many RO events are subject to such interfered GNSS measurements, which are considerably difficult to extract from the GNSS RO measurements. To precisely identify interfered RO events, an improved machine learning approach—a gradient descent artificial neural network (ANN)-aided radio-holography method—is proposed in this paper. Since this method is more complex than most other machine learning methods, for improving its efficiency through the reduction in computational time for near-real-time applications, a scale factor and a regularization factor are also adjusted in the ANN approach. This approach was validated using Constellation Observing System for Meteorology, Ionosphere, and Climate/FC-3 atmPhs (level 1b) data during the period of day of year 172–202, 2015, and its detection results were compared with the flag data set provided by Radio Occultation Meteorology Satellite Application Facilities for the performance assessment and validation of the new approach. The results were also compared with those of the support vector machine method for improvement assessment. The comparison results showed that the proposed method can considerably improve both the success rate of GNSS RO reflection detection and the computational efficiency.

[1]  Georg Beyerle,et al.  Observation and simulation of direct and reflected GPS signals in Radio Occultation Experiments , 2001 .

[2]  Peter L. Bartlett,et al.  Boosting Algorithms as Gradient Descent in Function Space , 2007 .

[3]  Xiaolei Zou,et al.  A quality control procedure for GPS radio occultation data , 2006 .

[4]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[5]  Ying Li,et al.  Dynamic statistical optimization of GNSS radio occultation bending angles: advanced algorithm and performance analysis , 2015 .

[6]  L. Barthes,et al.  Separation of multiple echoes using a high‐resolution spectral analysis for SuperDARN HF radars , 1998 .

[7]  J. Schofield,et al.  Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System , 1997 .

[8]  S. B. Healy,et al.  A modification to the standard ionospheric correction method used in GPS radio occultation , 2015 .

[9]  J. Wickert,et al.  GPS Radio occultations with CHAMP : A radio holographic analysis of GPS signal propagation in the troposphere and surface reflections , 2002 .

[10]  Judith E. Dayhoff,et al.  Conference on Prognostic Factors and Staging in Cancer Management: Contributions of Artificial Neural Networks and Other Statistical Methods , 2001 .

[11]  Jens Wickert,et al.  Application of radio holographic method for observation of altitude variations of the electron density in the mesosphere/lower thermosphere using GPS/MET radio occultation data , 2002 .

[12]  Sergey Sokolovskiy,et al.  Quality assessment of COSMIC/FORMOSAT-3 GPS radio occultation data derived from single- and double-difference atmospheric excess phase processing , 2010 .

[13]  Jaume Sanz,et al.  Ionospheric Tomography with GPS Data from CHAMP and SAC-C , 2005 .

[14]  Estel Cardellach,et al.  Carrier phase delay altimetry with GPS‐reflection/occultation interferometry from low Earth orbiters , 2004 .

[15]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[16]  Prabhat,et al.  Artificial Neural Network , 2018, Encyclopedia of GIS.

[17]  Norbert Jakowski,et al.  Radio occultation data analysis by the radioholographic method , 1999 .

[18]  R. O. Schmidt,et al.  Multiple emitter location and signal Parameter estimation , 1986 .

[19]  Estel Cardellach,et al.  Meteorological information in GPS-RO reflected signals , 2011 .

[20]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[21]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[22]  Stig Syndergaard,et al.  On the ionosphere calibration in GPS radio occultation measurements , 2000 .

[23]  Y. Wang,et al.  An investigation of atmospheric temperature profiles in the Australian region using collocated GPS radio occultation and radiosonde data , 2011 .

[24]  Ying-Hwa Kuo,et al.  A feasibility study of the radio occultation electron density retrieval aided by a global ionospheric data assimilation model , 2012 .

[25]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[26]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.