Benefits of INS/GPS Integration

1. Benefits of INS/GPS Integration Douglas Aguilar Marcin Kolodziejczak

2. INS Defined • An inertial navigation system is a navigation aid that uses motion sensors to continuously track the position, orientation, and velocity (direction and speed of movement) of a vehicle without the need for external references • Initial position and velocity must be provided before computing its own position and velocity by integrating information from sensors

3. Applications Aerial Surveying The Northrop Grumman Navigation Systems Division (NSD) LN-260 is a Form, Fit, and Function replacement INS/GPS for the F-16.

4. Strapdown Inertial System • Sensors mounted into device • Output quantities measured in body frame ωxb ωyb ωzb fxb fyb fzb

5. INS/GPS Advantage • INS • Integration of data results in long-wavelength errors • GPS • low data output rate in receivers, difficult to maintain accuracy at the centimeter level resulting in short-wavelength errors • Benefits • Precise continuous positioning of a moving platform • INS complements GPS, aids in positioning solution in events of cycle slips and signal losses

6. Tight vs Loose Integration • Single blended navigation solution from pseudorange, pseudorange rate, accelerations, gyro measurements gives more accurate solution than loosely coupled system • Tightly integrated system continues to extract info from GNSS receiver even when fewer than 4 satellites are visible

7. Loosely Coupled INS • The MIDG II is a loosely coupled system

8. Tight Integration

9. MEMS • Micro-Electro-Mechanical Systems (MEMS) • Built using silicon micro-machining techniques • Uses Coriolis effect using vibrating elements • Fc -Force m -mass w -angular velocity v –velocity • Advantages • Small size, low weight, low power, inexpensive to produce • Disadvantages • MEMS less accurate than fiber-optic based or ring laser gyros • Complex algorithms needed to generate solutions • Loses accuracy quickly due to bias drift characteristics

10. MEMS Gyroscope

11. MIDG Operation Modes • Vertical Gyro (VG) mode • Data from rate sensors is used for attitude estimation • IMU mode provides calibrated values for: • Angular rate • Acceleration • Magnetic field • Position and velocity available directly from GPS receiver only up to 5Hz

12. MIDG Info • Drift in position after GPS signal • Position accuracy degrades according to*: • HPacc = 0.1*T^2 + 2 • T (time) is in seconds • HPacc (horizontal position accuracy) is in meters • The HPacc equation represents a very basic curve fit of typical MIDG II accuracy estimate (1 sigma, conservative) based on collected data from several trials in which GPS was lost and the INS continued to estimate position without position measurements from GPS. *Based on data analysis from Microbotics

13. Mobile GPS Laboratory 3-Axis Rate Gyro 3-Axis Accelerometer 3-Axis Magnetometer

14. Data from 1181-1283 sec. 22sec

15. Nav vs GPS Delta X

16. Nav vs GPS Delta Y

17. Delta from 17-62 sec.

18. Delta from 134-164 sec.

19. Delta’s from Rondo

20. Conclusions • INS solution valid for about 20 seconds during GPS outages • INS + GPS did not significantly improve accuracy using the MIDG-INS • Y-axis for Nav was closer to kinematic solution than X-axis data • Data during GPS outage followed theoretical trend

21. References Inside GNSS Magazine • Jan/Feb 2007, GNSS solutions, “What is the difference between ‘lose’, ‘tight’, ‘ultra-tight’ and ‘deep’ integration strategies for INS and GNSS?” • Jan/Feb 2008, GNSS solutions, “MEMS and Platform Orientation & Deep Integration of GNSS/Intertial Systems.” Research Papers • Juan A. Fernandez-Rubio, “Performance Analysis of an INS/GPS Integrated System Augmented with EGNOS.” Universitat Politecnica de Catalunya, Barcelona, Spain 2004. • Vikas Kumar, “Integration of Inertial Navigation System and Global Positioning System Using Kalman Filtering.” Indian Institute of Technology, Bombay, Mumbai. July 2004 • Salah Sukkarieh, “Low Cost, High Integrity, Aided Inertial Navigation Systems for Autonomous Land Vehicles.” Department of Mechanical and Mechatronic Engineering, University of Sydney. March 2000 • Erik A. Wan, “Sigma-Point Kalman Filter based Integrated Navigation Systems.” OGI School of Science and Engineering at OHSU • Christopher Hide, Terry Moore, “GPS and Low Cost INS Integration for Positioning in the Urban Environment.” University of Nottingham • Kevin J. Walchko, Michael C. Nechyba, Eric Schwartz, Antonio Arroyo, “ Embedded Low Cost Intertial Navigation System.” University of Florida • Oliver J Woodman, “An Introduction to Inertial Navigation.” University of Cambridge. August 2007 • Isaac Skog and Peter Handel, “A Low-cost GPS Aided Inertial Navigation System for Vehicle Applications.” KTH Signals, Sensors and Systems, Royal Institute of Technology. Sweden • Mensur Omerbashich, “Integrated INS/GPS Navigation from a Popular Perspective.” University of New Brunswick. Canada. Journal of Air Transportation Vol. 7, No. 1 2002 • Michael Cramer, “GPS/INS Integration.” http://www.ifp.uni-stuttgart.de/publications/phowo97/cramer.pdf • John L. Crassidis, “Sigma-Point Kalman Filtering for Integrated GPS and Inertial Navigation.” University of Buffalo, State Univ. of New • York. Books • Christopher Jekeli, ‘Inertial Navigation Systems with Geodetic Applications.’ Walter de Gruyter, New York. 2001 • Paul Zarchan, ‘Global Positioning System: Theory and Applications Volumes I and II’’ AIAA,1996

22. Backup Slides • Additional Information

23. MIDG Output • Source • Column Packet Description • ------ ------ ----------- • 1 STATUS status word • 2 STATUS temperature (0.01 deg C) • 3 NAV_SENSOR Time (ms) • 4-6 NAV_SENSOR p,q,r angular rates (0.01 deg/s) • 7-9 NAV_SENSOR ax,ay,az accelerations (mili-g) • 10-12 NAV_SENSOR yaw,pitch,roll (0.01 deg) • 13 NAV_SENSOR flags • 14 (NAV_PV) boolean: NAV_PV data updated • 15-17 NAV_PV Position (as defined in NAV_PV Details) • 18-20 NAV_PV Velocity (as defined in NAV_PV Details) • 21 NAV_PV Details • 22 (NAV_ACC) boolean: NAV_ACC data updated • 23-24 NAV_ACC H/V Position accuracy estimate (cm) • 25-26 NAV_ACC H/V Velocity accuracy estimate (cm/s) • 27 NAV_ACC Tilt accuracy estimate (0.01 deg) • 28 NAV_ACC Heading accuracy estimate (0.01 deg) • 29 NAV_ACC flags • 30 (GPS_PV) boolean: GPS_PV data updated • 31 GPS_PV Time (ms) • 32 GPS_PV GPS Week • 33 GPS_PV Details • 34-36 GPS_PV Position (as defined by GPS_PV Details) • 37-39 GPS_PV Velocity (as defined by GPS_PV Details) • 40 GPS_PV PDOP (0.01 scaling) • 41 GPS_PV PAcc (cm) • 42 GPS_PV VAcc (cm/s)

24. MIDG Specifications

25. MIDG Specifications

26. MEMs Gyro Errors

27. MEMs Accelerometer Errors

28. MEMS Structure • MEMS less accurate than fiber-optic based or ring laser gyros • Filters and extra sensors can aid in accuracy • Complex algorithms needed to generate solutions • Losses accuracy quickly due to bias drift characteristics • AHRS-Attitude and heading reference system

29. MIDG Performance • GPS outages or signal degradation 1-3 satellites • The MIDG continues to provide position and velocity updates during GPS outages for a period of about 30 seconds*.  After that, the MIDG reverts to a vertical gyro mode in which only the attitude, rates, and accelerations are provided* *statement from Microbotics

30. MIDG Info • The MIDG II is "Differential Ready GPS" what does that mean and how would we use this feature? Additionally, there is no mention of WAAS in the "MIDG II Operating Modes" description, how (or when) is this feature activated? • The MIDG II supports both satellite based differential corrections (WAAS, EGNOS) and local RTCM corrections. If WAAS satellites are within view, their signal will be used to provide differential corrections. • Position accuracy without WAAS/EGNOS is 5-7m CEP and 2m CEP with WAAS/EGNOS (theoretically)

31. MIDG Info • The GPS receiver in the MIDG II is a 16 channel receiver. • Kalman filter has more than 16 inputs

32. MIDG Specification (0.055m/s)

33. RT 3100 Position Performance