The landscape of Global Navigation Satellite Systems (GNSS) has evolved substantially since its infancy in the 1970’s with the launch of GPS. Now, the landscape of space-borne signals is complicated by a sea of other GNSS constellations like Galileo, GLONASS, and BeiDou. My research focuses on creating a software environment to study and implement methods for building multifrequency, multi constellation GNSS receivers capable of utilizing the sea of information tied up in these constellations to outperform the single constellation receivers of olden times.
Within the scope of the portion of the so-called, “software-defined receiver (SDR),” presented here is the acquisition and infancy of signal tracking for such a software environment. This involves the study of random signals, radio interference, and signal processing to create a robust basis for this SDR. The SDR is able to carry raw binary files that mimic the receiver antenna through a gauntlet of filtering and tracking techniques to produce an image of your place within the constellations of GPS and Galileo. Within this undertaking was confronting the nuanced issues of creating a software capable of interpreting and identifying a series of signals that are tremendously different in makeup and modulation. Further, this software environment creates a repeatable tool for studying issues like signal jamming, spoofing, and navigating a signal from space to our smartphones without being lost in a sea of interference.
My name is Logan Bednarz and I’m a master’s student in mechanical and aerospace engineering with a specialization in systems analysis and controls at the Illinois Institute of Technology. My area of research is in GNSS technology and related topics in autonomous navigation. Right now, I’m focused on the study of GNSS signal acquisition and tracking to produce pseudorange results in environments of heavy RF interference and intentional spoofing attacks. Before I began work in the area of GNSS, I worked on the creation of the IIT UFarm. This project included the creation of an automated community gardening space utilizing technologies like remote sensing and data reduction, neural network image processing, and indoor HVAC studies.