Open Source MATLAB GPS Software

The Orbit Determination Toolbox (ODTBX) is developed by the Navigation & Mission Design branch at NASA Goddard Space Flight Center as an advanced mission simulation and analysis tool used for concept exploration, proposal, early design phase, and/or rapid design center environments. ODTBX functions and utilities are combined in a flexible architecture that allows for modular development of navigation algorithms and simulations. The core ODTBX functionality is realized through a set of estimation commands that incorporate Monte Carlo data simulation, linear covariance analysis, and measurement processing at a generic level.

The EPC software calculates velocities for any points located on the earth’s surface given the Euler pole parameters and estimates the Euler pole parameters given the observed velocities of a set of sites located on a tectonic plate. Since version 2, the software is able to estimate or apply the Plate Translation Rates, as well. Please cite one of the following papers when you use the software: - Goudarzi, M A, Cocard, M, and Santerre, R. 2014."EPC: Matlab Software to Estimate Euler Pole Parameters". GPS Solutions 18 (1): 153–62. DOI: 10.1007/s10291-013-0354-4*. - Goudarzi, M A. 2025."Evaluating Euler Pole Parameters for the North American Terrestrial Reference Frame of 2022". Geodesy and Geodynamics. DOI: 10.1016/j.geog.2025.03.004. * The element G_23 of the Jacobian matrix of linearized observations in formula 18 (page 4) in this paper has an incorrect sign (minus sign should be removed). This is discussed in: https://doi.org/10.1007/s10291-025-01932-7.

GPS Date Converter (GDC) is a program to convert six different date formats, including: (1) Decimal year, (2) Year, Month, Day, (3) Year, Day of year, (4) GPS Week, Day of week, (5) Julian date, and (6) Modified Julian date. These formats are quiet common in Earth Sciences, specially GPS applications. More information can be found in the GDC website.

eviltransform is a compact library for converting geographic coordinates between the global WGS-84 system (used by GPS) and the Chinese GCJ-02 system (used by many Chinese map services such as Google Maps China and AutoNavi). The project implements forward and inverse transformations (WGS→GCJ and GCJ→WGS) as well as a higher-precision inverse method (GCJ→WGS exact) that trades speed for sub-meter accuracy, and it includes a utility for computing geodesic distance in meters. It’s implemented (or ported) across many languages and platforms — you’ll find reference implementations in Go, C/C++/Obj-C, Java, JavaScript, Python, PHP, C#, Haskell, Rust, Swift, MATLAB and more — which makes it easy to drop into server, client, or mobile codebases. The README documents the API signatures and describes expected numeric accuracy (e.g., ~1–2 m for the standard inverse, <0.5 m for the exact inverse) and when to use each routine.

This project hosts the code and the documents which were created by Francesco Montorsi during his PhD at the University of Modena and Reggio Emilia, Italy, about indoor and outdoor localization and navigation techniques. The code (mostly MATLAB code) is publicly available to enable reproducible research.

Along with calculating basic statistics and quality parameters such as mean and variance, the soft-ware is capable of importing and visualizing different standard time series formats, determining and removing jumps and outliers, and makes numerical and publication quality graphical outputs. Bivariate statistical analysis (including correlation coefficient and linear regression) and time series analysis (including auto and cross-spectral analysis, wavelet power spectrum, spectrogram and periodicities) form the main analysis features of the software.

OpenPMU is a collaborative project developing a software defined Phasor Measurement Unit for Smart Grids and electrical utilities. This project is currently hosted by Queen’s University Belfast.

Our project consist in porting positioning algorithms on a GPU. We will improve programs which are already working on CPU in order to make them compatible with the CUDA technology offered by Nvidia. The advantage of this technology is that it allows us to use massive multithreading and so make calculations go faster. Algorithms will be implemented in C++.