Reading RINEX Ephemeris Files: Data Parsing and Array Storage in MATLAB

This document outlines the comprehensive workflow for processing RINEX (Receiver Independent Exchange) ephemeris and observation files using MATLAB. The implementation consists of multiple systematic steps for data acquisition, parsing, and storage. The process begins with downloading RINEX format files from GPS ground stations, which contain satellite orbital parameters (ephemeris data) and observational measurements. These standard text-based files follow specific formatting conventions that require specialized parsing routines. In MATLAB implementation, we utilize both built-in and custom functions to handle the file reading process. Key functions include textscan() or fgetl() for line-by-line parsing, with regular expressions to extract numerical values from the header and data sections. The parsing algorithm typically involves identifying data markers, validating file structure, and converting ASCII values to double-precision numerical arrays. The core processing involves converting the extracted data into structured arrays or matrices. For ephemeris data, this creates arrays containing satellite orbital elements (semi-major axis, eccentricity, inclination, etc.) with associated time stamps. Observation data is stored in multidimensional arrays encoding pseudorange, carrier phase, and signal strength measurements for each satellite-receiver pair. MATLAB's array manipulation capabilities enable efficient data organization using cell arrays or structured arrays (struct) to maintain metadata relationships. This array-based storage facilitates subsequent data analysis operations such as satellite position calculation, observation preprocessing, and quality control checks. Finally, MATLAB's visualization tools (plot, scatter, plot3) allow for comprehensive data plotting and graphical analysis. This includes satellite sky plots, observation time series, and orbital trajectory visualizations, providing crucial insights for GPS data validation and scientific interpretation. This complete workflow demonstrates fundamental GPS data processing techniques, forming the basis for advanced applications like precise point positioning, satellite clock analysis, and atmospheric studies. The MATLAB environment provides an ideal platform for implementing these algorithms with its robust numerical computing capabilities and specialized toolboxes for GNSS data processing.