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

copyright 2011 controltrix corp www. controltrix.com

Global Positioning System ++ improved GPS using sensor data fusion

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


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


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


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


• 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

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

Proposed method Advantages.


Sl No metric Kalman Filter Modified Filter

1. Drift •Drift is a major problem (depends inversely on K)•Needs considerable characterization.(Offset, temperature calibration etc).

•Guaranteed automatic convergence. •No prior measurement of offset and characterization required.•Not sensitive to temperature induced variable drift etc.

2. Convergence •Non-Zero measurement and process noise covariance required else leads to singularity

•Always converges•No assumptions on variances required •Never leads to a singular solution

3. Method •Two distinct phases: Predict and update.

•Can be implemented in a few single difference equation or even in continuum.

Comparison


Comparison.

Note: The right column filter is a super set of a standard Kalman filter

Sl No metric Kalman Filter Modified Filter4. Computation •Need separate state

variables for position, velocity, etc which adds more computation.

•Highly optimized computation.•Only single state variable required

5. Gain value /performance

•In one dimension, •K = process noise / measurement noise. dt • ‘termed as optimal’

•Gains based on systematic design choices. •The gains are good though suboptimal (based on tradeoff)

6. Processor req. •Needs 32 Bit floating point computation for accuracy and plenty of MIPS/ computation

•Easily implementable in 16 bit fixed point processor 40 MIPS/computation is sufficient


Experimental results

Stationary object• Red X - Raw GPS data• Green – Accelerometer integration(dead reckoning) • Blue Sensor fusion result



Thank [email protected]

Technology

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