Blind Opportunistic Navigation: Cognitive Deciphering of Partially Known Signals of Opportunity

Mohammad Neinavaie, Joe Khalife, and Zaher M. Kassas

Abstract: 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.
Published in: Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
September 21 - 25, 2020
Pages: 2748 - 2757
Cite this article: Neinavaie, Mohammad, Khalife, Joe, Kassas, Zaher M., "Blind Opportunistic Navigation: Cognitive Deciphering of Partially Known Signals of Opportunity," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), , September 2020, pp. 2748-2757.
https://doi.org/10.33012/2020.17592
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