Collaborative Opportunistic Navigation

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. Collaborative use of signals of opportunity can make navigation information more accessible and robust. Global Navigation Satellite System (GNSS) is insufficient for reliable and accurate anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). Traditional approaches to overcome the limitations of GNSS-based navigation entail coupling GNSS receivers with inertial navigation sensors. Motivated by the plenitude of ambient radio frequency signals in today’s environment (e.g., cellular, Iridium satellite, HDTV, AM/FM, WiFi), a new navigation paradigm is emerging. This paradigm, termed opportunistic navigation (OpNav), aims to exploit these signals of opportunity (SOPs) for navigation by extracting from them relevant positioning and timing information. In collaborative opportunistic navigation (COpNav), multiple receivers, whether handheld or vehicle-mounted, share information to construct and continuously refine a global signal landscape within which the receivers localize themselves in space and time. The first figure illustrates a system-level vision of COpNav, in which receivers on unmanned aerial and ground vehicles (UAV and UGV) and in a hand-held device share their observations of various SOPs over a communications network. The shared data is processed at a cloud-hosted signal landscape map database and a fusion center. Information is fed-back from the fusion center to aid signal tracking at each receiver. In its most general form, OpNav treats all ambient radio signals as potential SOPs, from conventional GNSS signals to communications signals never intended for use as timing or positioning sources. Each signal’s relative timing and frequency offsets, transmit location, and frequency stability are estimated on-the-fly as necessary, with prior information about these quantities exploited when available. At this level of generality, the OpNav estimation problem is similar to the simultaneous localization and mapping (SLAM) problem in robotics. Both imagine an agent which, starting with incomplete knowledge of its location and surroundings, simultaneously builds a map of its environment and locates itself within that map. In traditional SLAM, the map that gets constructed as the agent (typically a robot) moves through the environment is composed of landmarks—walls, corners, posts, etc.—with associated positions. OpNav extends this concept to radio signals, with SOPs playing the role of landmarks. In contrast to a SLAM environmental map, the OpNav signal landscape is more complex— it is dynamic and stochastic. 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 localization and mapping? (iv) What level of collaboration between the receivers achieves a minimal price of anarchy? Our work addressed the above fundamental questions and validated the theoretical conclusions numerically and experimentally via navigation software-defined receivers (SDRs). Dissertation: http://radionavlab.ae.utexas.edu/publications/366-analysis-and-synthesis... Papers: http://radionavlab.ae.utexas.edu/publications/355-receding-horizon-traje... http://radionavlab.ae.utexas.edu/publications/365-greedy-motion-planning... http://radionavlab.ae.utexas.edu/publications/333-observability-analysis...