Global Positioning System ++_Improved GPS using sensor data fusion


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Global Positioning System ++_Improved GPS using sensor data fusion

  1. 1. Global Positioning System ++ improved GPS using sensor data fusion www.controltrix.comcopyright 2011 controltrix corp www.
  2. 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 developerscopyright 2011 controltrix corp www.
  3. 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 noisycopyright 2011 controltrix corp www.
  4. 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 DRIFTcopyright 2011 controltrix corp www.
  5. 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 modificationcopyright 2011 controltrix corp www.
  6. 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 accelerometerscopyright 2011 controltrix corp www.
  7. 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 errorcopyright 2011 controltrix corp www.
  8. 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.
  9. 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 filtercopyright 2011 controltrix corp www.
  10. 10. Experimental results Stationary object • Red X - Raw GPS data • Green – Accelerometer integration(dead reckoning) • Blue Sensor fusion resultcopyright 2011 controltrix corp www.
  11. 11. copyright 2011 controltrix corp www.
  12. 12. Thank You consulting@controltrix.comcopyright 2011 controltrix corp www.