Robust GPS and radar sensor fusion for multiple aerial vehicles localization

This paper presents a novel localization framework for multiple AVs based on sensor fusion of global positioning system (GPS) and the identification friend-or-foe (IFF) Radar system. The IFF-Radar play two roles: firstly, it detects the angle and the range between two AVs, and thus the path between the two vehicles can be modeled as a curve. Secondly, it detects which AV helps form the above curve, and receives the corresponding GPS information from the friend vehicle. Through the cooperation of the multiple AVs, GPS can work well with fewer satellites due to the block of canyon environments. Analysis shows that two GPS satellites are sufficient to obtain the location information with the help of two or more friend vehicles. Simulations show that the proposed approach can achieve better localization by the cooperation among multiple aerial vehicles than single aerial vehicle.

[1]  Dan S. Necsulescu,et al.  Extended Kalman filter-based sensor fusion for operational space control of a robot arm , 2002, IEEE Trans. Instrum. Meas..

[2]  A. Farina,et al.  Tracking a ballistic target: comparison of several nonlinear filters , 2002 .

[3]  Abdullah Al Mamun,et al.  Sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints , 2007, Robotics Auton. Syst..

[4]  Peter Daly,et al.  Real time differential GPS and GLONASS vehicle positioning in urban areas , 1997, Proceedings of Conference on Intelligent Transportation Systems.

[5]  Krishan K. Ahuja,et al.  Radar-acoustic interaction for IFF applications , 1999, Proceedings of the 1999 IEEE Radar Conference. Radar into the Next Millennium (Cat. No.99CH36249).

[6]  Shuzhi Sam Ge,et al.  Queues and artificial potential trenches for multirobot formations , 2005, IEEE Transactions on Robotics.

[7]  Shuzhi Sam Ge,et al.  Autonomous vehicle positioning with GPS in urban canyon environments , 2001, IEEE Trans. Robotics Autom..

[8]  Seong Yun Cho,et al.  Robust positioning technique in low-cost DR/GPS for land navigation , 2006, IEEE Transactions on Instrumentation and Measurement.

[9]  Shuzhi Sam Ge,et al.  New potential functions for mobile robot path planning , 2000, IEEE Trans. Robotics Autom..

[10]  Mike P. Stewart,et al.  Urban canyon vehicle navigation with integrated GPS/GLONASS/DR systems , 1999 .

[11]  Herman Bruyninckx,et al.  Kalman filters for non-linear systems: a comparison of performance , 2004 .

[12]  Pedro Lima.  A Bayesian Approach to Sensor Fusion in Autonomous Sensor and Robot Networks , 2007, IEEE Instrumentation & Measurement Magazine.

[13]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[14]  P. Valin,et al.  Position and attribute fusion of radar, ESM, IFF and Datalink for AAW missions of the Canadian Patrol Frigate , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[15]  Zhaodan Kong,et al.  A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance , 2010, J. Intell. Robotic Syst..

[16]  E. Groten,et al.  Integrated INS/GPS system for high precision navigation applications , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[17]  Inseok Hwang,et al.  Intent-Based Probabilistic Conflict Detection for the Next Generation Air Transportation System , 2008, Proc. IEEE.

[18]  Li Bai,et al.  A Sensor Fusion Framework Using Multiple Particle Filters for Video-Based Navigation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[19]  James K. Kuchar,et al.  A review of conflict detection and resolution modeling methods , 2000, IEEE Trans. Intell. Transp. Syst..

[20]  Jianghai Hu,et al.  Application of Reachability Analysis for Stochastic Hybrid Systems to Aircraft Conflict Prediction , 2008, IEEE Transactions on Automatic Control.

[21]  R. R. Jr. Carson,et al.  Fusion of IFF and radar data , 1997, 16th DASC. AIAA/IEEE Digital Avionics Systems Conference. Reflections to the Future. Proceedings.

[22]  Brian J. Sauser,et al.  System of Systems Collaborative Formation , 2010, IEEE Systems Journal.

[23]  Jean-Yves Tourneret,et al.  A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation , 2010, IEEE Transactions on Signal Processing.

[24]  J. Stewart,et al.  Supplementing primary radar data with IFF for a practical demonstration of data fusion , 2000 .

[25]  F. W. Cathey,et al.  The iterated Kalman filter update as a Gauss-Newton method , 1993, IEEE Trans. Autom. Control..

[26]  D. J. Peters,et al.  Fusion of IFF and radar data , 1996, Proceeding of 1st Australian Data Fusion Symposium.

[27]  N. Aouf,et al.  Robust INS/GPS Sensor Fusion for UAV Localization Using SDRE Nonlinear Filtering , 2010, IEEE Sensors Journal.

[28]  Shuzhi Sam Ge,et al.  Multirobot Formations Based on the Queue-Formation Scheme With Limited Communication , 2007, IEEE Transactions on Robotics.

[29]  Peng Chang,et al.  Collision sensing by stereo vision and radar sensor fusion , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[30]  Cristina Urdiales,et al.  A New Efficiency-Weighted Strategy for Continuous Human/Robot Cooperation in Navigation , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[31]  Jiancheng Fang,et al.  Predictive Iterated Kalman Filter for INS/GPS Integration and Its Application to SAR Motion Compensation , 2010, IEEE Transactions on Instrumentation and Measurement.

[32]  J. Farrell,et al.  The global positioning system and inertial navigation , 1999 .