Collaborative Opportunistic Navigation


COpNav 

Overview

Opportunistic navigation (OpNav) aims to overcome the limitations of global navigation satellite systems (GNSS)-based navigation in GNSS-challenged environments, such as indoors, deep urban canyons, and intentionally-jammed environments. OpNav receivers continuously search for ambient radio frequency signals of opportunity (SOPs), such as cell phone signals, high-definition television signals, and iridium satellite signals, from which to draw navigation information, employing on-the-fly signal characterization as necessary. Signals from discovered SOPs are down-mixed and sampled coherently, yielding a tight coupling between signal streams that permits carrier-phase-level fusion of observables from the various streams within a single or distributed state estimator. In collaborative opportunistic navigation (COpNav), multiple OpNav receivers share information to construct and continuously refine a global signal landscape [1]-[3].

Observability and Estimability Analyses

We analyzed the observability of various scenarios that a COpNav environment comprising multiple receivers and multiple SOPs could exhibit. Subsequently the observability conditions under which a COpNav environment is fully observable were derived. Notions of nonlinear and linear observability of dynamic systems were invoked to rigorously prove such conditions. For scenarios where the COpNav environment was not fully observable, the unobservable directions in the state space were specified. Moreover, the degree of observability, also known as estimability, of the various states was assessed. The observability and estimability results were verified through numerical simulations and experimental demonstrations via software-defined radio (SDR) [4]-[6].

Adaptive Estimation of SOPs

We developed two adaptive filters for on-the-fly signal characterization of SOPs: maximum-likelihood (ML) and interacting multiple model (IMM). Not only these filters estimate the states of an unknown SOP, but also estimate the statistical model governing the SOP's clock error states. Our experimental demonstration to estimate the states and statistics of an unknown cellular code division multiple access (CDMA) tower showed an eight-meter improvement in the tower's location estimate over standard fixed filters [7].

Motion Planning for Optimal Information Gathering

We proposed various optimal greedy receiver motion planning strategies for simultaneous signal landscape mapping and receiver localization. Moreover, we showed that through proper reformulation, the greedy strategies can be recast as convex programs, the solutions of which are computationally efficient and suitable for receivers with limited processing power. Furthermore, we extended the greedy strategies to receding horizon strategies and studied their effectiveness and limitations. In addition, we synthesized various information fusion and decision making architectures for collaborative signal landscape mapping with a negligible price of anarchy [8]-[12].

SOP-Based Navigation with TDMA Signals

We proposed a technique for reconstructing a continuous phase time history from the non-continuous phase bursts of time division multiple access (TDMA) signals (e.g., Iridium satellites signals). A continuous phase time history facilitates exploitation of TDMA signals as SOPs within an OpNav framework. Because of their widespread use and availability in today's wireless communication market, TDMA signals are attractive candidates for OpNav. The phase reconstruction technique combined an integer least squares technique for estimating phase ambiguities at the beginning of each TDMA phase burst with a Kalman filter and smoother for removing these ambiguities and optimally stitching the bursts together [13]-[14].

Related Publications

  1. Analysis and synthesis of collaborative opportunistic navigation systems
    Z. Kassas
    Ph.D. Dissertation, The University of Texas at Austin, USA, May 2014

  2. Collaborative opportunistic navigation
    Z. Kassas
    IEEE Aerospace and Electronic Systems Magazine, Vol. 28, Issue 6, Jun. 2013, pp. 38-41

  3. Tightly-coupled opportunistic navigation for deep urban and indoor positioning
    K. Pesyna, Z. Kassas, J. Bhatti, and T. Humphreys
    ION Global Navigation Satellite Systems Conference, Sep. 20-23, 2011, Portland, OR, pp. 3605-3617

  4. Observability analysis of collaborative opportunistic navigation with pseudorange measurements
    Z. Kassas and T. Humphreys
    IEEE Transactions on Intelligent Transportation Systems, Vol. 15, Issue 1, Feb. 2014, pp. 260-273

  5. Observability and estimability of collaborative opportunistic navigation with pseudorange measurements
    Z. Kassas and T. Humphreys
    ION Global Navigation Satellite Systems Conference, Sep. 17-21, 2012, Nashville, TN, pp. 621-630

  6. Observability analysis of opportunistic navigation with pseudorange measurements
    Z. Kassas and T. Humphreys
    AIAA Guidance, Navigation, and Control Conference, Aug. 13-16, 2012, Minneapolis, MN, pp. 4760-4775

  7. Adaptive estimation of signals of opportunity
    Z. Kassas, V. Ghadiok, and T. Humphreys
    ION Global Navigation Satellite Systems Conference, Sep. 8-12, 2014, Tampa, FL, pp. 1679-1689

  8. Greedy motion planning for simultaneous signal landscape mapping and receiver localization
    Z. Kassas, A. Arapostathis, and T. Humphreys
    IEEE Journal of Selected Topics in Signal Processing,Vol. 9, Issue 2, March 2015, pp. 247-258

  9. Receding horizon trajectory optimization in opportunistic navigation environments
    Z. Kassas and T. Humphreys
    IEEE Transactions Aerospace and Electronic Systems, accepted

  10. The price of anarchy in active signal landscape map building
    Z. Kassas and T. Humphreys
    IEEE Global Conference on Signal and Information Processing, Dec. 3-5, 2013, Austin, TX, pp. 165-168

  11. Receding horizon trajectory optimization for simultaneous signal landscape mapping and receiver localization
    Z. Kassas, J. Bhatti, and T. Humphreys
    ION Global Navigation Satellite Systems Conference, Sep. 16-20, 2013, Nashville, TN, pp. 1962-1969

  12. Motion planning for optimal information gathering in opportunistic navigation systems
    Z. Kassas and T. Humphreys
    AIAA Guidance, Navigation, and Control Conference, Aug. 19-22, 2013, Boston, MA, pp. 4551-4565

  13. A phase-reconstruction technique enabling low-power centimeter-accurate mobile positioning
    K. Pesyna, Z. Kassas, R. Heath, and T. Humphreys
    IEEE Transactions on Signal Processing, Vol. 62, Issue 10, May 2014, pp. 2595-2610

  14. Constructing a continuous phase time history from TDMA signals for opportunistic navigation
    K. Pesyna, Z. Kassas, and T. Humphreys
    IEEE Position, Location, and Navigation Symposium, Apr. 24-26, 2012, Myrtle Beach, SC, pp. 1209-1220