University of California, Riverside

Department of Electrical and Computer Engineering

Collaborative Opportunistic Navigation

Collaborative Opportunistic Navigation

Collaborative Opportunistic Navigation

February 25, 2014 - 2:10 pm
Winston Chung Hall, 205/206


Navigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Navigation system failure in these systems due to jamming, spoofing, or otherwise will have intolerable consequences.

Global Navigation Satellite Systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks. The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is proposed. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape.

For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers’ objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) What is the minimal required a priori knowledge about the environment for full observability? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) What motion planning strategy should the receivers employ for optimal information gathering? (iv) What level of collaboration between the receivers achieves a minimal price of anarchy?

Our work addresses the above fundamental questions and validates the theoretical conclusions numerically and experimentally.


Zak Kassas is a Ph.D. candidate in the Department of Electrical and Computer Engineering at The University of Texas at Austin and a graduate research assistant at The Radionavigation Laboratory. He received a B.S with Honors 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 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 Corp. From 2008 through 2011 he was an adjunct professor at Texas State University. He has published over twenty refereed journal and conference articles and holds one U.S. patent. His research interests include estimation, navigation, autonomous vehicles, and intelligent transportation systems


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