Integrated Inertial, GPS, and LORAN System

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Integrated Inertial, GPS, and LORAN System

• 1/C Hunter T. Atherton
• Project Advisors CAPT Hartnett,
• Mr. Johnson, LT Teixeira
• Sponsor FAA and DHS

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Background

• Aviation and Maritime systems have become
  dependent on GPS
• GPS is vulnerable to intentional and
  unintentional jamming as well as blockages
• New Project in Spring 2004, IMU Acquisition

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Three Nav Methods

• Brief Nav System Breakdown
• LORAN Long Range Navigation
• GPS Global Positioning System
• IMU Inertial Measurement Unit
• Systems Integration
• Kalman Filtering
• Benefits of Systems Integration
• Data Integrity
• Ability to Coast through outages

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Kalman Filtering

• Minimum mean squared estimate
• Approximates a best available solution based on
  several different inputs (i.e. IMU, GPS, LORAN)
• Similar to Dead Reckoning on a Ship
• Original paper written by R.E. Kalman in 1960

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Objectives

• Develop Kalman integrated IMU-GPS-LORAN system
• Decide on the flavor of Nav Integration Loose
  or Tight
• Understand IMU
• Understand GPS
• Integrate IMU and GPS
• Understand LORAN
• Integrate IMU, GPS, and LORAN
• Test system on various seagoing assets and
  airframes

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Loosely Coupled System
Dynamics Conversion
IMU
3D fix
3D fix
GPS
Accurate, Reliable Fix
Kalman Filter
2D fix
LORAN
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The IMU

• Crossbow IMU400CC
• MEMS
• 6DOF
• Continuous
• and Polled
• Serial Coms
• Low Cost

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Dynamics Conversion

• World Space Local Space
• Gravity Cancellation
• Integration
• Angular Rates Single Integration
• Linear Accelerations Double Integration

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Dynamics Conversion
Z-1
Velocity
OldVelocity
Moves From Local Space to World Space
Present_Tran Present_Rot
Find_New
Z-1
Theta A
Theta A
NewTran NewRot
Freq
Theta A
Kalman Filter
W AA
CCData
Num_int_trap
Remove Gravity
Num_int_trap
Removes 1g From the negative Z axis (world space)
Obtains data from IMU
Single integrate W
Double integrate A And apply to previous
Displacement Vector
CubePlot_3d
Plots the Cube in Real Time
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Integration

• Marginally Stable
• The present biases, combined with the DC gain
  produce exponential displacement values

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Can the Bias be Tracked?
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Demonstration
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Project Evaluation

• Developed dynamics data conversion algorithm for
  IMU
• Analyzed Feasibility of Loosely Coupled System
• New Direction Tightly coupled system
• First Effort GPS IMU Integrated Nav System
• More Distant Goals GPS IMU LORAN Integrated
  Nav System

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Pseudorange Fix

• Raw Novatel data Rinex data Matlab Matrices
• Satellite Locations are not Constant
• Ephemeris
• Almanac
• Minimum Satellites for a fix 4
• 4 Equations 4 Unknowns
• Ionospheric and Tropospheric Corrections
• GPS Toolbox in Matlab
• Beta Version Needed lots of Modifications
• A single pseudorange can now be used

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GPS
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Tightly Coupled System
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Tightly Coupled System
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Results

• IMU Debugging Algorithm Demonstration
• GPS Satellite Pseudoranges
• Measurement space converted to position space
• Kalman Integrated GPS and IMU

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Conclusions/Future Direction

• Tightly coupled Kalman integrated navigation
  system can provide a valuable navigation tool for
  sea and air-based assets.
• We have a tightly coupled system, but its not
  fully debugged.
• We will Develop understanding of the intricacies
  of LORAN
• Eventually finish full Kalman Integration with
  all three systems inputs.