1. Global Positioning System ++
improved GPS using sensor data fusion
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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
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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
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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
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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
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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
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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
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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.
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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
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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