Resilient and Accurate Navigation for Autonomous Vehicles

April 10, 2015, Webb 1100

Zak Kassas

UC Riverside, Electrical and Computer Engineering


The steady trend towards autonomous vehicles will come with a demand for full situational awareness and extremely reliable and accurate navigation systems. With no human in the loop, the cost of navigation system failure will be severe. Reliance on GPS for navigation has become a single point of failure. The recent uptick in GPS jamming and spoofing incidents have exposed the vulnerability of GPS-based navigation and demonstrated the necessity for a complementary navigation system. We propose a new navigation framework that we call Collaborative Opportunistic Navigation. In this framework, radios collaboratively draw relevant positioning and timing information from ambient signals of opportunity (SOPs) to build and continuously refine a global signal landscape map of the environment within which they simultaneously localize themselves in space and time. For the sake of motivation, consider the following problem. A number of radios withno a priori knowledge about their own states are dropped in an environment comprisingmultiple unknown SOPs. The radios draw pseudorange observations from the SOPs. Theradios’ objective is to build a high-fidelity signal landscape map of the environment withinwhich they localize themselves in space and time. We then ask: (1) What is the minimalrequired a priori knowledge about the environment for full observability? (2) How shouldwe characterize recently discovered SOPs on-the-fly? (3) How can the fidelity of the signallandscape map and spatio-temporal radio localization accuracy be improved via (i) motionplanning and (ii) deployment of multiple radios? (4)What collaborative filtering architectureis best for sharing information among radios?

Speaker's Bio

Zak Kassas is an Assistant Professor of Electrical and Computer Engineering at The University of California, Riverside. He received a B.E. in Electrical Engineering from the Lebanese American University, an M.S. in Electrical and Computer Engineering from The Ohio State University, and an M.S.E. in Aerospace Engineering and a Ph.D. in Electrical and Computer Engineering from The University of Texas at Austin. From 2004 through 2010 he was a research and development engineer with the LabVIEW Control Design and Dynamical Systems Simulation Group at National Instruments Corporation. He has published over twenty refereed journal and conference articles, holds a U.S. patent, and is a senior member of the IEEE. His research interests include estimation, navigation, autonomous vehicles, target tracking, and intelligent transportation systems.