Mohammad Neinavaie, Joe Khalife, and Zaher M. Kassas, University of California, Irvine

View Abstract Sign in for premium content


A blind opportunistic navigation (BON) framework is proposed. This framework deciphers partially known signals of opportunity (SOPs) in a cognitive fashion. BON enables acquisition and tracking of terrestrial or space-based SOPs with minimal prior knowledge about their beacon signal. A computationally efficient algorithm is presented to blindly decode the beacon signals and estimate the Doppler frequency. The BON framework is applied to decipher the C/A pseudorandom noise (PRN) sequences from four GPS satellites. Experimental results are presented demonstrating a percentage of correctly decoded chips for these four PRN sequences ranging between 91% and 99%. These deciphered sequences are fed to a software-defined Rx (SDR) which produce a two-dimensional (2-D) position error of 54.5m for a stationary antenna.