The Performance Analysis of the Tactical Inertial Navigator Aided by Non-GPS Derived References

The Inertial Navigation System (INS) is now widely applied in many navigation and mobile mapping applications due to its high sampling rates, high accuracy in short-term cases, and no limitations caused by interference or signal obstructions. In addition, the INS can continuously provide the position, velocity and attitude of a vehicle. Conversely, the disadvantage of the stand-alone INS is that its accuracy degrades rapidly with time because of the accumulations of systematic errors and noises from accelerometers and gyroscopes. Therefore, this research aims to implement an integrated system with specific 3D position updates using non-GPS derived references to aid a tactical inertial navigator to provide seamless navigation solutions in the specific area without Global Positioning System (GPS) signals. An Extended Kalman Filter (EKF) is applied as the core estimator to provide superior performance and output the navigation solutions in real-time. The INS is updated by position from references such as the digital map, land mark, Digital Terrain Model (DTM) as well as waypoint to improve navigation accuracy in the long-term. In order to evaluate the performance of the proposed algorithm, field tests including land scenario in freeway and airborne scenario with an unmanned aerial test platform have been conducted. The preliminary results demonstrate that the proposed algorithm with non-GPS derived references aiding from digital map and waypoint for onboard aerial camera trigger to provide uninterrupted navigation solutions and better performance which can achieve the meter-level accuracy without GPS aiding for land and aerial scenarios, respectively.

[1]  C. Striebel,et al.  On the maximum likelihood estimates for linear dynamic systems , 1965 .

[2]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[3]  Samer S. Saab,et al.  A map matching approach for train positioning. I. Development and analysis , 2000, IEEE Trans. Veh. Technol..

[4]  C. Jekeli Inertial navigation systems with geodetic applications , 2000 .

[5]  S. Saab A Map Matching Approach for Train Positioning Part I : Development and Analysis , 2000 .

[6]  Adrijana Car,et al.  Road Reduction Filtering for GPS‐GIS Navigation , 2001, Trans. GIS.

[7]  C. Jekeli 10 Geodetic Application , 2001 .

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

[9]  Washington Y. Ochieng,et al.  A general map matching algorithm for transport telematics applications , 2003 .

[10]  J. Junkins,et al.  Optimal Estimation of Dynamic Systems , 2004 .

[11]  Eun-Hwan Shin,et al.  Navigation kalman filter design for pipeline pigging , 2005 .

[12]  John Weston,et al.  Strapdown Inertial Navigation Technology, Second Edition , 2005 .

[13]  Wu Chen,et al.  Improving Integrity and Reliability of Map Matching Techniques , 2006 .

[14]  Robert B. Noland,et al.  A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport , 2006, J. Intell. Transp. Syst..

[15]  Mohammed A. Quddus,et al.  High integrity map matching alogorithms for advanced transport telematics applications , 2007 .

[16]  Xiaoji Niu,et al.  Improving the Performance of Portable Navigation Devices by Using Partial IMU Based GPS/INS Integration Technology , 2008 .

[17]  Kai-Wei Chiang,et al.  An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications , 2008, Appl. Soft Comput..

[18]  John Krumm,et al.  Hidden Markov map matching through noise and sparseness , 2009, GIS.

[19]  Kai-Wei Chiang,et al.  The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications , 2012, Sensors.

[20]  Tsung-Ming Chen,et al.  The Performance Analysis of a Real-Time Integrated INS / GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination , 2014 .