Zaher M. Kassas

Tightly-Coupled Opportunistic Navigation for Deep Urban and Indoor Positioning

A strategy is presented for exploiting the frequency stability, transmit location, and timing information of ambient radio-frequency “signals of opportunity” for the purpose of navigating in deep urban and indoor environments. The strategy, referred to as tightly-coupled opportunistic navigation (TCON), involves a receiver continually searching for signals from which to extract navigation and timing information. The receiver begins by characterizing these signals, whether downloading characterizations from a collaborative online database or performing characterizations on-the-fly. Signal observables are subsequently combined within a central estimator to produce an optimal estimate of position and time. A simple demonstration of the TCON strategy focused on timing shows that a TCONenabled receiver can characterize and use CDMA cellular signals to correct its local clock variations, allowing it to coherently integrate GNSS signals beyond 100 seconds.

Observability Analysis of Collaborative Opportunistic Navigation With Pseudorange Measurements

The observability analysis of a collaborative opportunistic navigation (COpNav) environment whose states may be partially known is considered. A COpNav environment can be thought of as a radio frequency (RF) signal landscape within which one or more RF receivers locate themselves in space and time by extracting and, possibly, sharing information from ambient signals of opportunity (SOPs). These receivers, whether vehicle mounted or integrated into handheld devices, exploit signal diversity to improve navigation and timing robustness compared with stand-alone Global Positioning System (GPS) receivers in deep urban, indoor, or, otherwise, GPS-hostile environments. Available SOPs may have a fully known, partially known, or unknown characterization. In this paper, the receivers are assumed to draw only pseudorange-type measurements from the SOPs. Separate observations are fused to produce an estimate of each receivers position, velocity, and time (PVT). Since not all SOP states in the COpNav environment may be known a priori, the receivers must estimate the unknown SOP states of interest simultaneously with their own PVT. This paper establishes the minimal conditions under which a COpNav environment consisting of multiple receivers and multiple SOPs is completely observable. Moreover, in scenarios where the COpNav environment is unobservable, the unobservable directions in the state space are specified. Simulation and experimental results are presented to confirm the theoretical observability conditions.

Constructing a continuous phase time history from TDMA signals for opportunistic navigation

A technique is developed for reconstructing a continuous phase time history from the noncontinuous phase bursts of time division multiple access (TDMA) signals. A continuous phase time history facilitates exploitation of TDMA signals as signals of opportunity (SOPs) within an opportunistic navigation framework. Because of their widespread use and availability in todays wireless communication market, TDMA signals are attractive candidate SOPs for opportunistic navigation. The phase reconstruction technique presented here combines 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. A Monte-Carlo-type simulation and test environment has been developed to investigate the sensitivity of the proposed phase reconstruction technique to various system parameters, namely, carrier-to-noise ratio, receiver clock quality, TDMA transmitter clock quality, line-of-sight acceleration uncertainty, and TDMA burst structure. Simulation results indicate that successful carrier phase reconstruction is most strongly dependent on the TDMA burst period and on the combined phase random walk effect of the receiver and transmitter clocks, the propagation effects, and the range errors.

Observability Analysis of Opportunistic Navigation with Pseudorange Measurements

The observability analysis of an opportunistic navigation (OpNav) environment whose states may be partially known is considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in space and time by extracting information from ambient signals of opportunity (SOPs). Available SOPs may have a fully-known, partially-known, or unknown characterization. In the present work, the receiver is assumed to draw only pseudorange-type measurements from the SOP signals. Separate observations are fused to produce an estimate of the receiver’s position, velocity, and time (PVT). Since not all SOP states in the OpNav environment may be known a priori, the receiver must estimate the unknown SOP states of interest simultaneously with its own PVT. The observability analysis presented here first evaluates various linear and nonlinear observability tools and identifies those that can be appropriately applied to OpNav observability. Subsequently, the appropriate tools are invoked to establish the minimal conditions under which the environment is observable. It is shown that a planar OpNav environment consisting of a receiver and n SOPs is observable if either the receiver’s initial state is known or the receiver’s initial position is known along with the initial state of one SOP. Simulation results are shown to agree with the theoretical observability conditions.

Motion Planning for Optimal Information Gathering in Opportunistic Navigation Systems

Motion planning for optimal information gathering in an opportunistic navigation (OpNav) environment is considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in space and time by extracting information from ambient signals of opportunity (SOPs). The receiver is assumed to draw only pseudorange-type observations from the SOPs, and such observations are fused through an estimator to produce an estimate of the receiver’s own states. Since not all SOP states in the OpNav environment may be known a priori, the receiver must estimate the unknown SOP states of interest simultaneously with its own states. In this work, the following problem is studied. A receiver with no a priori knowledge about its own states is dropped in an unknown, yet observable, OpNav environment. Assuming that the receiver can prescribe its own trajectory, what motion planning strategy should the receiver adopt in order to build a high-fidelity map of the OpNav signal landscape, while simultaneously localizing itself within this map in space and time? To answer this question, first, the minimum conditions under which the OpNav environment is fully observable are established, and the need for receiver maneuvering to achieve full observability is highlighted. Then, motivated by the fact that not all trajectories a receiver may take in the environment are equally beneficial from an information gathering point of view, a strategy for planning the motion of the receiver is proposed. The strategy is formulated in a coupled estimation and optimal control framework of a gradually identified system, where optimality is defined through various information-theoretic measures. Simulation results are presented to illustrate the improvements gained from adopting the proposed strategy over random and pre-defined receiver trajectories.

Observability and Estimability of Collaborative Opportunistic Navigation with Pseudorange Measurements

The observability and estimability of collaborative opportunistic navigation (COpNav) environments are studied. A COpNav environment can be thought of as a radio frequency signal landscape within which one or more radio frequency receiver locate themselves in space and time by extracting and possibly sharing information from ambient signals of opportunity (SOPs). Available SOPs may have a fully-known, partially-known, or unknown characterization. In the present work, the receivers are assumed to draw only pseudorange-type measurements from the SOPs. Separate observations are fused to produce an estimate of each receiver’s position, velocity, and time (PVT). Since not all SOP states in the COpNav environment may be known a priori, the receivers must estimate the unknown SOP states of interest simultaneously with their own PVT. This paper establishes the minimal conditions under which a COpNav environment consisting of multiple receivers and multiple SOPs is completely observable. In scenarios where the COpNav environment is not completely observable, the observable states, if any, are specified. Moreover, for the completely observable scenarios, the degree of observability, commonly referred to as estimability, of the various states is studied, with particular attention paid to the states with exceptionally good and poor observability.

Greedy Motion Planning for Simultaneous Signal Landscape Mapping and Receiver Localization

Greedy motion planning strategies to enhance situational awareness in an opportunistic navigation (OpNav) environment are considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in time and space by extracting information from ambient signals of opportunity (SOPs). The receiver is assumed to draw only pseudorange observations from the SOPs. The following problem is considered. A receiver with no a priori knowledge about its own initial states nor the initial states of multiple SOPs, except for one, is dropped in an OpNav environment. Assuming that the receiver can prescribe its maneuvers, what greedy (i.e., one-step look-ahead) motion planning strategy should the receiver adopt so to optimally build a high-fidelity signal landscape map of the environment while simultaneously localizing itself within this map in time and space with high accuracy? Several information-based and innovation-based motion planning strategies are studied. It is shown that with proper reformulation, the innovation-based strategies can be cast as tractable convex programs, the solution of which is computationally efficient. Simulation results are presented comparing the various strategies and illustrating the improvements gained from adopting the proposed strategies over random and predefined receiver trajectories.

Adaptive Estimation of Signals of Opportunity

To exploit unknown ambient radio frequency signals of opportunity (SOPs) for positioning and navigation, one must estimate their states along with a set of parameters that characterize the stability of their oscillators. SOPs can be modeled as stochastic dynamical systems driven by process noise. The statistics of such process noise is typically unknown to the receiver wanting to exploit the SOPs for positioning and navigation. Incorrect statistical models jeopardize the estimation optimality and may cause filter divergence. This necessitates the development of adaptive filters, which provide a significant improvement over fixed filters through the filter learning process. This paper develops two such adaptive filters: an innovationbased maximum likelihood filter and an interacting multiple model filter and compares their performance and complexity. Numerical and experimental results are presented demonstrating the superiority of these filters over fixed, mismatched filters.

Collaborative opportunistic navigation [Student research highlight]

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 information from ambient radio frequency signals of opportunity (SOPs). OpNav radio receivers continuously search for opportune 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 landscape.

New Mechatronics Development Techniques for FPGA-Based Control and Simulation of Electromechanical Systems

Abstract Field programmable gate arrays (FPGAs) have been widely adopted in high volume commercial applications, but not as much in the industrial control and simulation arenas. Due to the attractive features of FPGAs, such as their inherent flexibility, performance, parallelism, and low-level reconfigurability, industrial control design and simulation vendors have been creating the next generation FPGA development tool chains that are designed for engineers with little or no digital design expertise. The goal of these next generation system-level design tools is to empower control design, simulation, and signal processing engineers to harness the full power of the FPGA technology, while providing relatively competitive performance and resource usage, as compared to traditional text-based hardware description level (HDL) methods. This paper discusses some of the traditional challenges that prohibited wide adoption of FPGAs in the industrial control and simulation fields, and how new graphical system design tools are helping mechatronics engineers leverage the full power of FPGAs as deployment platforms. Moreover, it discusses some particularly useful development techniques for FPGA-based control and simulation in mechatronics applications.