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Global Positioning System ++
              improved GPS using sensor data fusion




                                  www.controltrix.com



copyright 2011 controltrix corp                         www. controltrix.com
Objective
  • Estimate position by augmenting GPS data with accelerometer +
         compass data
  • Data more accurate than GPS
  • Under unreliable GPS signal estimate position
  • Create API for smartphone app developers




copyright 2011 controltrix corp                            www. controltrix.com
GPS
  •      Satellite Triangulation based method
  •      Requires signals from 4 or more satellites
  •      Accuracy ~ 10 m
  •      Data rate about once few seconds
  •      System is blind between samples
  •      GPS Data tends to jump around and is noisy




copyright 2011 controltrix corp                       www. controltrix.com
Accelerometer
  •      Smart phones have 3 axis MEMS accelerometer + compass
  •      Integrating accelerometer data gives velocity
  •      Integrating velocity gives position
  •      a.k.a Dead Reckoning
  •      Offset and random walk of MEMS causes DRIFT




copyright 2011 controltrix corp                                  www. controltrix.com
Sensor fusion
  •      Kalman filter with optimal gain K for sensor data fusion
  •      Estimate by combining GPS and acc. measurement
  •      Standard well known solution
  •      Augmented by modification




copyright 2011 controltrix corp                                     www. controltrix.com
Proposed method Advantages
   •      No matrix calculations
   •      Easier computation, can be easily scaled
   •      Equivalent to Kalman filter structure (easily proven)
   •      No drift (the error converges to 0)
   •      Estimate accelerometer drift in the system by default
   •      Drift est. for calib. and real time comp. of accelerometers




copyright 2011 controltrix corp                                         www. controltrix.com
Proposed method Advantages.
   • Can be modified easily to make tradeoff between drift
     performance (convergence) and noise reduction
   • Systematic technique for parameter calculations
   • No trial and error




copyright 2011 controltrix corp                              www. controltrix.com
Comparison
    Sl No metric                  Kalman Filter           Modified Filter
    1.            Drift       •Drift is a major problem   •Guaranteed automatic convergence.
                              (depends inversely on K)    •No prior measurement of offset and
                              •Needs considerable         characterization required.
                              characterization.(Offset,   •Not sensitive to temperature induced
                              temperature calibration     variable drift etc.
                              etc).
    2.            Convergence •Non-Zero measurement       •Always converges
                              and process noise           •No assumptions on variances required
                              covariance required else    •Never leads to a singular solution
                              leads to singularity
    3.            Method          •Two distinct phases:   •Can be implemented in a few single
                                  Predict and update.     difference equation or even in
                                                          continuum.



copyright 2011 controltrix corp                                                        www. controltrix.com
Comparison.
    Sl No metric                 Kalman Filter                   Modified Filter
    4.    Computation            •Need separate state            •Highly optimized computation.
                                 variables for position,         •Only single state variable required
                                 velocity, etc which adds more
                                 computation.
    5.            Gain value     •In one dimension,              •Gains based on systematic design
                  /performance •K = process noise /              choices.
                                 measurement noise. dt           •The gains are good though
                                 • ‘termed as optimal’           suboptimal (based on tradeoff)
    6.            Processor req. •Needs 32 Bit floating point    •Easily implementable in 16 bit
                                 computation for accuracy        fixed point processor 40
                                 and plenty of MIPS/             MIPS/computation is sufficient
                                 computation



     Note: The right column filter is a super set of a standard Kalman filter
copyright 2011 controltrix corp                                                            www. controltrix.com
Experimental results




  Stationary object
  • Red X - Raw GPS data
  • Green – Accelerometer integration(dead reckoning)
  • Blue Sensor fusion result
copyright 2011 controltrix corp                         www. controltrix.com
copyright 2011 controltrix corp   www. controltrix.com
Thank You
                                  consulting@controltrix.com




copyright 2011 controltrix corp                                www. controltrix.com

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Global Positioning System ++ : Improved GPS using sensor data fusion

  • 1. Global Positioning System ++ improved GPS using sensor data fusion www.controltrix.com copyright 2011 controltrix corp www. controltrix.com
  • 2. Objective • Estimate position by augmenting GPS data with accelerometer + compass data • Data more accurate than GPS • Under unreliable GPS signal estimate position • Create API for smartphone app developers copyright 2011 controltrix corp www. controltrix.com
  • 3. GPS • Satellite Triangulation based method • Requires signals from 4 or more satellites • Accuracy ~ 10 m • Data rate about once few seconds • System is blind between samples • GPS Data tends to jump around and is noisy copyright 2011 controltrix corp www. controltrix.com
  • 4. Accelerometer • Smart phones have 3 axis MEMS accelerometer + compass • Integrating accelerometer data gives velocity • Integrating velocity gives position • a.k.a Dead Reckoning • Offset and random walk of MEMS causes DRIFT copyright 2011 controltrix corp www. controltrix.com
  • 5. Sensor fusion • Kalman filter with optimal gain K for sensor data fusion • Estimate by combining GPS and acc. measurement • Standard well known solution • Augmented by modification copyright 2011 controltrix corp www. controltrix.com
  • 6. Proposed method Advantages • No matrix calculations • Easier computation, can be easily scaled • Equivalent to Kalman filter structure (easily proven) • No drift (the error converges to 0) • Estimate accelerometer drift in the system by default • Drift est. for calib. and real time comp. of accelerometers copyright 2011 controltrix corp www. controltrix.com
  • 7. Proposed method Advantages. • Can be modified easily to make tradeoff between drift performance (convergence) and noise reduction • Systematic technique for parameter calculations • No trial and error copyright 2011 controltrix corp www. controltrix.com
  • 8. Comparison Sl No metric Kalman Filter Modified Filter 1. Drift •Drift is a major problem •Guaranteed automatic convergence. (depends inversely on K) •No prior measurement of offset and •Needs considerable characterization required. characterization.(Offset, •Not sensitive to temperature induced temperature calibration variable drift etc. etc). 2. Convergence •Non-Zero measurement •Always converges and process noise •No assumptions on variances required covariance required else •Never leads to a singular solution leads to singularity 3. Method •Two distinct phases: •Can be implemented in a few single Predict and update. difference equation or even in continuum. copyright 2011 controltrix corp www. controltrix.com
  • 9. Comparison. Sl No metric Kalman Filter Modified Filter 4. Computation •Need separate state •Highly optimized computation. variables for position, •Only single state variable required velocity, etc which adds more computation. 5. Gain value •In one dimension, •Gains based on systematic design /performance •K = process noise / choices. measurement noise. dt •The gains are good though • ‘termed as optimal’ suboptimal (based on tradeoff) 6. Processor req. •Needs 32 Bit floating point •Easily implementable in 16 bit computation for accuracy fixed point processor 40 and plenty of MIPS/ MIPS/computation is sufficient computation Note: The right column filter is a super set of a standard Kalman filter copyright 2011 controltrix corp www. controltrix.com
  • 10. Experimental results Stationary object • Red X - Raw GPS data • Green – Accelerometer integration(dead reckoning) • Blue Sensor fusion result copyright 2011 controltrix corp www. controltrix.com
  • 11. copyright 2011 controltrix corp www. controltrix.com
  • 12. Thank You consulting@controltrix.com copyright 2011 controltrix corp www. controltrix.com