Benefits of INS/GPS Integration

1
Benefits of INS/GPS Integration

• Douglas Aguilar
• Marcin Kolodziejczak

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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

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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.
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Strapdown Inertial System

• Sensors mounted into device
• Output quantities measured in body frame

?xb ?yb ?zb
fxb fyb fzb
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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

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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

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Loosely Coupled INS

• The MIDG II is a loosely coupled system

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Tight Integration
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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

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MEMS Gyroscope
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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

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MIDG Info

• Drift in position after GPS signal
• Position accuracy degrades according to
• HPacc 0.1T2 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

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Mobile GPS Laboratory
3-Axis Rate Gyro 3-Axis Accelerometer 3-Axis
Magnetometer
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Data from 1181-1283 sec.
22sec
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Nav vs GPS Delta X
16
Nav vs GPS Delta Y
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Delta from 17-62 sec.
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Delta from 134-164 sec.
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Deltas from Rondo
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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

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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/phowo
  97/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

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Backup Slides

• Additional Information

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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)

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MIDG Specifications
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MIDG Specifications
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MEMs Gyro Errors
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MEMs Accelerometer Errors
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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

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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

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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)

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MIDG Info

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

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MIDG Specification
(0.055m/s)
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RT 3100 Position Performance