Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion

Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.

[1]  Kil To Chong,et al.  Design of an EKF-CI based sensor fusion for robust heading estimation of marine vehicle , 2015 .

[2]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.

[3]  Lynn Huh,et al.  Kalman Filter for Beginners: with MATLAB Examples , 2011 .

[4]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[5]  Rudolph van der Merwe,et al.  Sigma-Point Kalman Filters for Integrated Navigation , 2004 .

[6]  Deok Jin Lee,et al.  A simulation of low cost GPS/INS integration using Extended Kalman filter , 2014 .

[7]  Valérie Renaudin,et al.  Use of Earth’s Magnetic Field for Mitigating Gyroscope Errors Regardless of Magnetic Perturbation , 2011, Sensors.

[8]  Ching-Yao Chan,et al.  Reliable vehicle sideslip angle fusion estimation using low-cost sensors , 2014 .

[9]  Denis Pomorski,et al.  GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects , 2006, Inf. Fusion.

[10]  Quentin Ladetto,et al.  Digital Magnetic Compass and Gyroscope for Dismounted Soldier Position & Navigation , 2002 .

[11]  Tristan Perez,et al.  Kinematic Models for Manoeuvring and Seakeeping of Marine Vessels , 2007 .

[12]  Zhen Li,et al.  Disturbance Compensating Model Predictive Control With Application to Ship Heading Control , 2012, IEEE Transactions on Control Systems Technology.

[13]  Walter Higgins,et al.  A Comparison of Complementary and Kalman Filtering , 1975, IEEE Transactions on Aerospace and Electronic Systems.

[14]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[15]  Chan Gook Park,et al.  Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System , 2009 .

[16]  M. U. de Haag,et al.  Integrating GPS/MEMS-based-IMU with single GPS baseline for improved heading performance , 2012, 2012 IEEE Aerospace Conference.

[17]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[18]  Jinling Wang,et al.  Integrated GPS/INS navigation system with dual-rate Kalman Filter , 2012, GPS Solutions.

[19]  P. Zhang,et al.  Navigation with IMU/GPS/digital compass with unscented Kalman filter , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[20]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[21]  Michal Reinstein,et al.  Complementary filtering approach to orientation estimation using inertial sensors only , 2012, 2012 IEEE International Conference on Robotics and Automation.

[22]  John L. Crassidis,et al.  Real-Time Attitude-Independent Three-Axis Magnetometer Calibration , 2005 .

[23]  Jang Gyu Lee,et al.  Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Tae Suk Yoo,et al.  Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System , 2011, Sensors.

[25]  Yong Liu.  Navigation and Control of Mobile Robot Using Sensor Fusion , 2010 .

[26]  Futoshi Kobayashi,et al.  Sensor selection based on fuzzy inference for sensor fusion , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).