Search

Exploiting the reflected signals from the surface of the Earth to provide information about the reflecting surface is the primary concept of GNSS Interferometric Reflectometry (GNSS-IR). One of the most common usages of GNSS-IR is the estimation of the absolute vertical distance from a GNSS antenna to the reflective surface. In this work, the authors present a developed MATLAB-based GNSS software, namely, GNSS-IR-UT that translates the various signal from different systems such as GPS (L1, L2 and L5), GLONASS (G1, G2 and G3), Galileo (E1, E5a, E5b, E5 and E6), and BeiDou (B1, B2 and B3), more precisely the signal-to-noise ratio (SNR) data, into the reflection height. This user-friendly software permits users to evaluate a variety of processing options and parameters in height calculation by taking advantage of the Graphic User Interface (GUI) of MATLAB. In this software, to extract the dominant frequency in reflectometry concept, the Least Square Harmonic Estimation (LS-HE) has been used for the first time. Moreover, besides the reflectometry applications, the users can use some parts of this software in working with GNSS data for their specific usage. All in all, we aimed at providing a new, easy to use, and powerful software to the scientific community to make it easy for new users of the GNSS-IR concept.

Today, satellite altimetry is capable of observing the instantaneous sea level and is regarded as a standard tool in the study of waters level variations on both regional and global scales. In this research, we have introduced a software package under MATLAB called ATSAT, which is used to process the satellite altimetry data. ATSAT is able to process the observations of five satellites, namely Jason-1, Jason-2, Jason-3, Saral and Sentinel-3. This software program has certain capabilities: 1) Reading the observations related to the five satellites in the NetCDF format 2) Producing the time series obtained from the observations of each satellite at any desired point 3) Analyzing the outlier detection in the time series 4) Estimating the weight matrix pertinent to each time series using the Least Square Variance Components Estimation (LS-VCE) method 5) Considering the two noise models of White and White + Flicker for the variance components estimation and formation of the weight matrix for each time series 6) Estimating the average water level, as well as the amplitude of 22 main tide components at any desired point 7) Extracting the available frequencies in each time series using the Least Square Harmonic Estimation (LS-HE) method in the two modes of univariate and multivariate, and finally 8) Calculating the radial orbital error using the crossover adjustment.