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.

[1]  Stergios I. Roumeliotis,et al.  Analysis and improvement of the consistency of extended Kalman filter based SLAM , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[3]  Kenneth M. Pesyna,et al.  Tightly-Coupled Opportunistic Navigation for Deep Urban and Indoor Positioning , 2011 .

[4]  Javier Ibanez Guzman,et al.  On the Observability and Observability Analysis of SLAM , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[6]  Gamini Dissanayake,et al.  Observability analysis of SLAM using fisher information matrix , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

[7]  Bradford W. Parkinson,et al.  Global positioning system : theory and applications , 1996 .

[8]  S. Sukkarieh,et al.  Observability analysis and active control for airborne SLAM , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[9]  A. Krener,et al.  Nonlinear controllability and observability , 1977 .

[10]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[11]  John L. Casti,et al.  Recent Developments and Future Perspectives in Nonlinear System Theory , 1982 .

[12]  Eric Nettleton,et al.  On the nonlinear observability and the information form of the SLAM problem , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Stergios I. Roumeliotis,et al.  Observability-based Rules for Designing Consistent EKF SLAM Estimators , 2010, Int. J. Robotics Res..

[14]  E. Nettleton,et al.  On the linear and nonlinear observability analysis of the SLAM problem , 2009, 2009 IEEE International Conference on Mechatronics.

[15]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. I. Theory , 1992 .

[16]  Juan Andrade-Cetto,et al.  The effects of partial observability in SLAM , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[17]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[18]  M. Anguelova Observability and identifiability of nonlinear systems with applications in biology , 2007 .

[19]  Z. M. Kassas,et al.  Discretisation of continuous-time dynamic multi-input multi-output systems with non-uniform delays , 2011 .

[20]  Juan Andrade-Cetto,et al.  The effects of partial observability when building fully correlated maps , 2005, IEEE Transactions on Robotics.

[21]  Robert W. Heath,et al.  Extending the reach of GPS-assisted femtocell synchronization and localization through Tightly-Coupled Opportunistic Navigation , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[22]  Teresa A. Vidal-Calleja,et al.  On the Observability of Bearing-only SLAM , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[23]  Wilson J. Rugh,et al.  Linear system theory (2nd ed.) , 1996 .

[24]  Mark L. Psiaki,et al.  Modeling, Analysis, and Simulation of GPS Carrier Phase for Spacecraft Relative Navigation , 2005 .