Optimal PRN Codes and Receiver Design for More Robust and Secure Satellite Navigation
Abstract
In this report, we present our work for more robust and secure satellite navigation: i) designing optimal Pseudo-Random Noise (PRN) codes; and receiver design using future Chimera Signals. For optimal PRN code design, we developed a Gaussian policy gradient-based reinforcement learning algorithm which constructs high-quality families of spreading code sequences. We have demonstrated the ability of our algorithm to achieve better mean-squared auto- and cross-correlation than well-chosen families of equal-length Gold codes and Weil codes. For receiver design using future Chimera Signals, we designed a method to provide continuous GPS signal verification between Chimera authentication times by using stochastic reachability analysis. We demonstrated that our spoofing detector probabilistically satisfies a user-defined false alarm requirement throughout the trajectory during nominal conditions, while demonstrating its ability to successfully detect spoofing during a simulated attack.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 28, 2022
- Accession Number
- AD1169384
Entities
People
- Grace Xingxin Gao
Organizations
- Stanford University