INS/GPS Aided by Frequency Contents of Vector Observations With Application to Autonomous Surface Crafts

This paper presents a high-accuracy, multirate inertial navigation system (INS) integrating global positioning system (GPS) measurements and advanced vector aiding techniques for precise position and attitude estimation of autonomous surface crafts (ASCs). Designed to be implemented and tested in the DELFIMx catamaran developed at ISR/IST, the navigation system comprises an advanced inertial integration algorithm to account for coning and sculling motions, combined with an extended Kalman filter (EKF) for inertial sensor error compensation. Aiding gravitational observations are optimally exploited in the EKF, by deriving a sensor integration technique that takes into account the vehicle's dynamics bandwidth information to properly trace measurement disturbances and extract the relevant sensor information. The proposed aiding technique and the performance of the navigation system are assessed using experimental data obtained at seatrials with a low-cost hardware architecture installed on-board the DELFIMx platform. It is shown that the low frequency information embodied in pendular measurements improves the compensation of inertial sensor bias and noise, and consequently enhances the performance of position and attitude estimation. The overall improvements obtained with the vector aiding observations are also illustrated for the case of GPS signal outage, emphasizing the extended autonomy of the navigation system with respect to position aiding.

[1]  Lyal B. Harris.  November , 1890, The Hospital.

[2]  P. Savage.  Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms , 1998 .

[3]  Kenneth R Britting,et al.  Inertial navigation systems analysis , 1971 .

[4]  G. Wahba.  A Least Squares Estimate of Satellite Attitude , 1965 .

[5]  Itzhack Bar-itzhack,et al.  On the Convergence of Iterative Orthogonalization Processes , 1976, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Carlos Silvestre,et al.  GPS aided IMU for Unmanned Air Vehicles 1 , 2004 .

[7]  Mario B. Ignagni,et al.  Duality of Optimal Strapdown Sculling and Coning Compensation Algorithms , 1998 .

[8]  Einar Berglund,et al.  Model-aided inertial navigation for underwater vehicles , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[10]  B.J. Buckham,et al.  A Kalman filter for the navigation of remotely operated vehicles , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[11]  C. Silvestre,et al.  GPS AIDED IMU FOR UNMANNED AIR VEHICLES , 2004 .

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

[13]  Paulo Oliveira,et al.  Study and implementation of an EKF GIB-based underwater positioning system , 2004 .

[14]  Carlos Silvestre,et al.  Improving Aiding techniques for USBL Tightly-Coupled Inertial Navigation System , 2008 .

[15]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[16]  Rita Cunha,et al.  A Coastline Following Preview Controller For the DELFIMx Vehicle , 2007 .

[17]  T. Aoki,et al.  An integrated navigation system for autonomous underwater vehicles with two range sonars, inertial sensors and Doppler velocity log , 2004, Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600).

[18]  Kelly M. Roscoe.  Equivalency Between Strapdown Inertial Navigation Coning and Sculling Integrals/Algorithms , 2001 .

[19]  Carlos Silvestre,et al.  Inertial Navigation System Aided by GPS and Selective Frequency Contents of Vector Measurements , 2005 .

[20]  F. Markley.  Attitude Error Representations for Kalman Filtering , 2003 .

[21]  Paulo Oliveira,et al.  Embedded Vehicle Dynamics and LASER Aiding Techniques for Inertial Navigation Systems , 2006 .

[22]  Asgeir J. Sørensen,et al.  Identification of Dynamically Positioned Ships , 1995 .

[23]  Jonghyuk Kim,et al.  Vehicle model aided inertial navigation , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[24]  John L. Crassidis,et al.  Survey of nonlinear attitude estimation methods , 2007 .

[25]  Itzhack Y. Bar-Itzhack,et al.  Optimized TRIAD Algorithm for Attitude Determination , 1996 .

[26]  M. Shuster,et al.  Complete linear attitude-independent magnetometer calibration , 2002 .

[27]  Rita Cunha,et al.  A PATH-FOLLOWING CONTROLLER FOR THE DELFIMX AUTONOMOUS SURFACE CRAFT , 2006 .

[28]  M. Shuster,et al.  Three-axis attitude determination from vector observations , 1981 .

[29]  J. Bortz.  A New Mathematical Formulation for Strapdown Inertial Navigation , 1971, IEEE Transactions on Aerospace and Electronic Systems.

[30]  João Alfredo Santos,et al.  TOOLS FOR THE DIAGNOSIS AND AUTOMATED INSPECTION OF SEMI-SUBMERGED STRUCTURES , 2002 .

[31]  P. Savage.  STRAPDOWN INERTIAL NAVIGATION INTEGRATION ALGORITHM DESIGN. PART 2: VELOCITY AND POSITION ALGORITHMS , 1998 .

[32]  Mark Koifman,et al.  Inertial navigation system aided by aircraft dynamics , 1999, IEEE Trans. Control. Syst. Technol..

[33]  M. Pittelkau.  Rotation Vector in Attitude Estimation , 2003 .

[34]  I. Bar-Itzhack,et al.  Observability analysis of piece-wise constant systems. II. Application to inertial navigation in-flight alignment (military applications) , 1992 .

[35]  L. Whitcomb,et al.  A SURVEY OF UNDERWATER VEHICLE NAVIGATION : RECENT ADVANCES AND NEW CHALLENGES , 2006 .

[36]  C. Foster,et al.  Extension of a Non-Linear, Two-Step Calibration Methodology to Include Non-Orthogonal Sensor Axes , 2006 .

[37]  Carlos Silvestre,et al.  INSPECTION AND DIAGNOSIS OF SINES' WEST BREAKWATER , 2005 .

[38]  G.H. Elkaim,et al.  Extension of a two-step calibration methodology to include nonorthogonal sensor axes , 2008, IEEE Transactions on Aerospace and Electronic Systems.