Carrier-phase GNSS Attitude Determination and Control for Small Unmanned Aerial Vehicle Applications

As part of our recent research to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicle (UAV) applications, we investigated the potential of carrier-phase Global Navigation Satellite System (GNSS) for attitude determination and control of small size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro- Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD)sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

[1]  Frank Kleijer,et al.  Troposphere Modeling and Filtering for Precise GPS Leveling , 2004 .

[2]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

[3]  Gabriele Giorgi,et al.  Carrier phase GNSS attitude determination with the Multivariate Constrained LAMBDA method , 2010, 2010 IEEE Aerospace Conference.

[4]  Itthisek Nilkhamhang,et al.  Path tracking of UAV using self-tuning PID controller based on fuzzy logic , 2010, Proceedings of SICE Annual Conference 2010.

[5]  Chansik Park,et al.  An Error Analysis of 2-Dimensional Attitude Determination System Using Global Positioning System , 2000 .

[6]  J. Keong,et al.  Determining heading and pitch using a single difference GPS/GLONASS approach , 1999 .

[7]  H. S. Hopfield Tropospheric Effect on Electromagnetically Measured Range: Prediction from Surface Weather Data , 1971 .

[8]  Gaurav S. Sukhatme,et al.  Vision-based autonomous landing of an unmanned aerial vehicle , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[9]  Andreas Wieser,et al.  Improved Positioning Accuracy with High-Sensitivity GNSS Receivers and SNR Aided Integrity Monitoring of Pseudo-Range Observations , 2005 .

[10]  F. Brunner,et al.  GPS signal diffraction modelling: the stochastic SIGMA-δ model , 1999 .

[11]  James Pinchin GNSS Based Attitude Determination for Small Unmanned Aerial Vehicles , 2011 .

[12]  Roberto Sabatini,et al.  Design and integration of vision based sensors for unmanned aerial vehicles navigation and guidance , 2012, Photonics Europe.

[13]  P. Axelrad,et al.  Gps Error Analysis , 1996 .

[14]  P. J. G. Teunissen,et al.  Instantaneous Global Navigation Satellite System (GNSS)-Based Attitude Determination for Maritime Applications , 2012, IEEE Journal of Oceanic Engineering.

[15]  P. Teunissen The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation , 1995 .

[16]  Zhichao Chen,et al.  Qualitative Vision-Based Path Following , 2009, IEEE Transactions on Robotics.

[17]  Bradford W. Parkinson,et al.  FLIGHT TESTS OF ATTITUDE DETERMINATION USING GPS COMPARED AGAINST AN INERTIAL NAVIGATION UNIT. , 1994 .

[18]  Paul Cross,et al.  A New Signal-to-Noise-Ratio Based Stochastic Model for GNSS High-Precision Carrier Phase Data Processing Algorithms in the Presence of Multipath Errors , 2006 .

[19]  Alison Kay Brown,et al.  Interferometric attitude determination using the global positioning system , 1981 .

[20]  Michael S. Braasch,et al.  GPS Interferometric Attitude and Heading Determination: Initial Flight Test Results , 1991 .

[21]  Dong-Hwan Hwang,et al.  Error Analysis of 3-Dimensional GPS Attitude Determination System , 2006 .

[22]  Xiaojin Gong,et al.  A Survey of Techniques for Detection and Tracking of Airport Runways , 2006 .

[23]  Ismael Colomina,et al.  New Approaches to IMU Modeling and INS/GPS Integration for UAV-Based Earth-Observation , 2008 .

[24]  Saurabh Godha,et al.  Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application , 2006 .

[25]  Sameh Nassar,et al.  Improving the Inertial Navigation System (INS) error model for INS and INS/DGPS applications , 2003 .

[26]  G. A. McGraw,et al.  Tropospheric error modeling for high integrity airborne GNSS navigation , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.