Raw GNSS Data Compression using Compressive Sensing for Reflectometry Applications

Global Navigation Satellite System (GNSS) is a passive and in-expensive technique for remote sensing applications. GNSS reflectometry (GNSS-R) aims at analyzing the multipath signal reflected from the surface surrounding the receiving antenna. One of the challenge is the size of the raw GNSS-R I/Q data. The data size is very large even for a short duration of time. To address this issue an algorithm is proposed using the Compressive Sensing (CS) theory, to compress and reduce the size of data that is to be transmitted for further processing. The data is first compressed using the CS theory and then reconstructed using convex l
₁
minimization algorithm. In addition, the performance of different sparsifying and measurement matrices that are used in the compression are compared and reconstruction of the original signal is performed. The algorithm proposed is verified for the raw Global Positioning System (GPS) and Navigation with Indian Constellation (NavIC) data set.