copyright 2011 controltrix corpwww. controltrix.com Global Positioning System ++ Improved GPS using sensor data fusion
copyright 2011 controltrix corpwww. 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 corpwww. 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 corpwww. 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 corpwww. 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 corpwww. controltrix.com 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 corpwww. controltrix.com 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.
copyright 2011 controltrix corpwww. controltrix.com Sl NometricKalman FilterModified 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.
copyright 2011 controltrix corpwww. controltrix.com Note: The right column filter is a super set of a standard Kalman filter Sl NometricKalman FilterModified Filter 4.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
copyright 2011 controltrix corpwww. controltrix.com Experimental results Stationary object Red X - Raw GPS data Blue Sensor fusion result Actual Position GPS Data
copyright 2011 controltrix corpwww. controltrix.com
copyright 2011 controltrix corpwww. controltrix.com Thank You