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Despite the extraordinary advances in global navigation sat-ellite systems (GNSS), the inherent limitation of the weak-ness of their space-based signals makes such signals easy to block intentionally or accidentally. This makes GNSS insuf-ficient for reliable anytime, anywhere navigation, particu-larly in GNSS-challenged environments, such as indoors, deep urban canyons, and GNSS-denied environments expe-riencing intentional jamming . Several approaches have been proposed to address this inherent limitation of GNSS-based navigation, most notably augmenting GNSS receiv-ers with dead-reckoning systems and map-matching algo-rithms [2,3]. These approaches typically fuse the outputs of a fixed number of well-modeled heterogeneous sensors, particularly GNSS receivers, inertial navigation systems, and digital map databases, with specialized signal process-ing algorithms.
Motivated by the plenitude of ambient radio frequency signals in GNSS-challenged environments, a new paradigm to overcome the limitations of GNSS-based navigation is proposed. This paradigm, termed opportunistic navigation (OpNav), aims to extract positioning and timing informa-tion from ambient radio frequency signals of opportunity (SOPs). OpNav radio receivers continuously search for op-portune signals from which to draw navigation and timing information, employing on-the-fly signal characterization as necessary . In collaborative opportunistic navigation (COpNav), multiple OpNav receivers share information to construct and continuously refine a global signal land-scape.
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 tim-
ing or positioning sources. Each signals 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 that, starting with incom-plete knowledge of its location and surroundings, simulta-neously 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 landmarkswalls, 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 land-scape is dynamic and more complex. For the simple case of pseudorange-only OpNav, where observables consist solely of signal time-of-arrival measurements, one must estimate, besides the position and velocity of each SOP transmitters antenna phase center, each SOPs time offset from a refer-ence time base, rate of change of time offset, and a set of parameters that characterize the SOPs reference oscillator stability. Even more SOP parameters are required for an OpNav framework in which both pseudorange and carrier phase measurements are ingested into the estimator . Of course, in addition to the SOP parameters, the OpNav re-ceivers own position, velocity, time offset, and time offset rate must be estimated.
The Global Positioning System (GPS) control segment routinely solves an instance of the COpNav problem: the location and timing offsets of a dozen or more GPS ground stations are simultaneously estimated with the orbital and clock parameters of GPS satellite vehicles (SVs). Compared to the general COpNav problem, the GPS control segments problem enjoys the constraints imposed by accurate prior estimates of site locations and SV orbits. Moreover, estima-tion of clock states is aided by the presence of highly-stable atomic clocks in the SVs and at each ground station. In con-trast, a COpNav receiver entering a new signal landscape may have less prior information to exploit and cannot as-sume atomic frequency references, neither for itself nor for the SOPs.
Figure 1 illustrates a COpNav environment in which two receivers share their observations on the various SOPs
Student Research HighlightCollaborative Opportunistic NavigationZaher M. Kassas University of Texas at Austin
Authors current address: Z. M. Kassas, Radionavigation Laboratory, The University of Texas at Austin, W. R. Woolrich Laboratories, 210 East 24th Street, Austin, TX 78712. E-mail address: firstname.lastname@example.org. Manuscript received October 23, 2012 and ready for publication December 5, 2012. Review handled by J. Glass. 0885/8985/13/ $26.00 2013 IEEE
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through a signal landscape map database and a fusion cen-ter. The signal landscape map and fusion center maintains the latest state estimates of the signal landscape states by fusing the observations made by the COpNav receivers. Relevant estimates are communicated back to each COpNav receiver.
Research questions pertaining to COpNav can be catego-rized into two classes: (i) optimal signal extraction and (ii) fundamental estimation questions.
Optimal signal extraction focuses on grouping the SOPs according to their modulation schemes so that the signals can be modeled appropriately for optimal signal extraction of parameters of interest for navigation pur-poses. For example, GPS, Galileo, and COMPASS GNSS, along with some cell phone carriers, modulate their sig-nals through code division multiple access (CDMA). The Iridium SVs communication system, global system for mobile communications (GSM), and high-definition television (HDTV) signals are modulated through time division multiple access (TDMA). In , it was demon-strated that the receivers time offset can be estimated by
Figure 1. COpNav environment illustration.
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Opportunist ic Navigat ion
exploiting CDMA signals from nearby cell phone towers. The obtained estimates were comparable to the estimates achieved by relying on GPS signals. To exploit TDMA sig-nals for carrier-phase-based navigation, one must address their intermittent nature and phase ambiguities. In , a technique was developed for reconstructing a continuous phase time history from the non-continuous phase bursts of TDMA signals.
Two fundamental estimation questions of COpNav are concerned with observability and estimability. Conceptually, observability of a dynamic system is a question of solvabil-ity of the states from a set of observations that are linearly or nonlinearly related to the states, and where the states evolve according to a set of linear or nonlinear difference or differential equations. While observability is a Boolean property (i.e., it asserts whether a system is observable or not) estimability quantifies the degree of observability of the various states. In  and , the minimum conditions un-der which a COpNav environment is completely observable were derived and the states estimability was quantified. It was shown that a planar COpNav environment compris-ing multiple receivers with velocity random walk dynamics making pseudorange measurements on multiple terrestrial static SOPs is completely observable if and only if the initial states of at least: (i) one receiver is fully known, (ii) one re-ceiver is partially known and one SOP is fully known, or (iii) one SOP is partially known and one SOP is fully known. The receivers state vector consisted of the receivers position xr and yr, velocity x r and y r , clock bias tr , and clock drift tr , whereas the SOPs state vector consisted of the SOPs posi-tion xs and ys, clock bias ts, and clock drift ts . Fully known refers to the knowledge of all the initial states in the state
vector, while partially known re-fers to the knowledge of the initial position states in the state vector.
A COpNav simulator was de-veloped in which the receivers noisy measurements were fused through an extended Kalman fil-ter to estimate the various states in the environment. Figure 2 shows results for case (i), defined previ-ously. As it is expected for an ob-servable system, the estimation er-ror trajectories of all the SOP states converged and were bounded by the estimation error variances 2. In Figure 2, c is the speed of light.
Future work will focus on op-timal signal extraction methods from other types of SOPs. Also, additional estimation architec-tural questions will be addressed, such as how to deal with dynami-cal and statistical environmental model uncertainties, and which
estimation architecture is appropriate: decentralized, cen-tralized, or hierarchical.
 Seco-Granados, G., Lopez-Salcedo, J., Jimenez-Banos, D., and Lopez-Risueno, G. Challenges in indoor Global Navigation Sat-ellite Systems, IEEE Signal Processing Magazine, Vol. 29, 2, (2012), 108-131.
 Groves, P. Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems. Norwood, MA: Artech House. 2008.
 Saab, S., and Kassas, Z. Power matching approach for GPS cov-erage extension. IEEE Transactions on Intelligent Transportation Systems, Vol. 7, no. 2, pp. 156166, Jun. 2006.
 Pesyna, K., Kassas, Z., Bhatti, J., and Humphreys, T. Tightly-coupled opportunistic navigation for deep urban and indoor positioning. In Proceedings of 24th Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Portland, OR, Sept. 2011, 36053616.
 Durrant-Whyte, H., and Bailey, T. Simultaneous localization and mapping: part I. IEEE Robotics and Automation Magazine, Vol. 13, 2 (2006), 99110.
 Pesyna, K., Kassas, Z., and Humphreys, T. Constructing a con-tinuous phase time history from TDMA signals for opportu-nistic navigation. In Proceedings of IEEE Position, Location, and Navigation Symposium (PLANS 2012), Myrtle Beach, SC, April 2012, 2426.
 Kassas, Z., and Humphreys, T. Observability analysis of op-portunistic navigation with pseudorange measurements. In Proceedings of AIAA Guidance, Navigation, and Control Conference
(AIAA GNC 2012), Minneapolis, MN, Aug. 2012, 47604775.
Figure 2. Estimation error trajectories and estimation error variances of the states of an unknown SOP.
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 Kassas, Z. and Humphreys, T. Observability and estimability of collaborative opportunistic navigation with pseudorange mea-surements. In Proceedings of 25th Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Nash-ville, TN, Sept. 2012-, 621-630.
Zaher (Zak) M. Kassas received a B.E. with Honors in Elec-trical Engineering from The Lebanese American University, a M.S. in Electrical and Computer Engineering from The Ohio State University, and a M.S.E. in Aerospace Engineer-ing from The University of Texas at Austin. He is currently a
Ph.D. candidate at The Uni-versity of Texas at Austin. From 2004 to 2010 he was a research and development engineer with the Control Design and Dynamical Sys-tems Simulation Group at National Instruments. His research interests include estimation and filtering, navigation, control sys-tems, and intelligent trans-portation systems.