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Benefits of INS/GPS Integration

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Benefits of INS/GPS Integration Douglas Aguilar Marcin Kolodziejczak INS Defined An inertial navigation system is a navigation aid that uses motion sensors to ... – PowerPoint PPT presentation

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Title: 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

?xb ?yb ?zb
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 0.1T2 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

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
Deltas 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/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

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)

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
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