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8/6/2019 EKF Kamkimlong

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Oct, 2010


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Content:

Part 1:

Part 2:

Part 3:

Indoor positioning based on RFIDsystem.

Outdoor positioning based on GPS.

Increasing the accuracy of

positioning by using EKF.


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Part 1 : Outdoor positioning

based on GPS.Overview of GPS.

The sources of positioning error.

Solutions to limit the positioning

error.


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Overview of GPS


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The sources of positioning error Propagated errors.

Ionopheric Propagation Errors.Tropospheric Propagation Errors.

Multipath Errors.

Satellite and Receiver causederrors.Satellite and Receiver clock errors.

Ephemeris Data Errors.

Mesurement noise at receiver.

Other errors.

Selective Availability Errors.Dilution of Precision.

Interference by other systems on the

ground.


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Limit errors and ensure channel.

Several methods ensure thechannel : LNA, PLL, Smartantennas

Several methods limit the errors:

DGPS technique.

Kalman Filter.Kalman Algorithm Diagram


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Part 2 : Indoor positioning

based on RFID system.

Indoor positioning requirement.

Using RFID for Positioning.

Build up Indoor positioning

model. model.


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Indoor positioning requirement

Indoor environments are being extended


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Limitation of GPS in indoor environment


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EKF


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Using RFID for Positioning.


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The RFID active chips will transmit

these data to readers:

The chips coordinates (in local

coordinates) and its identification.

The nominal value of transmitting

power.

The parameters in IEEE 802.11 thatsupporting to correct distance

measurements in each specific

environment.

1). First Model

Build up Indoor positioning model.


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2). Second Model

The RFID active chips will beattached to users.

Users will move in space thatarranged with RFID readers.

These readers will beconnected to data fusioncenter. This center willdetermine users coordinatesand send the result to usersreceiver by other channel link.


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Part 3 : Increasing the accuracy

of positioning by using EKF.

Kalman algorithm.

Linear Kalman Filter.

Extended Kalman Filter.


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Generally, Kalman algorithm is a group ofmathematical equations described anefficient recurrence method for state

estimation of process. It is optimal in thesense that it minimizes the estimated errorcovariance, when some presumedconditions are met.


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Kalman Algorithm

State vector

X(k)

Observation

vector

Z(k)

?KalmanMinimizes theestimated error

covariance

Model of System

State estimatedvector


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Linear Kalman Filter


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Linear Kalman Filter (cont)


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Extended Kalman Filter


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Extended Kalman Filter (cont)


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Using EKF for increasing the accuracy

of GPS.

Kalman


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Tracking Outdoor

Simulation

Si l t t ki U t j t i


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Red curvesimulates usersmotion.

Green curve

simulatescalculatedtrajectory of userreceiver withoutEKF.

Blue curvesimulatescalculatedtrajectory of userreceiver in EKFmodel.

Simulate tracking Users trajectory in

outdoor environment


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Green points:positioning errors

without EKF.

Red points:positioning errors

in EKF model.

Errors in outdoor positioning.


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Comments on simulation results:

The maximum error is about 5 meters in caseusing EKF model, whereas 23 meters in casewithout EKF.

Trajectory of user receiver in EKF model is closerto trajectory of users motion than trajectory ofuser receiver without EKF. (Show on F. 1)

The average estimation error of EKF is very small

than without EKF case. However, several pointsin curve are under suddenly changing errors.

According to the result, it shows that thepositioning errors are reduced significantly.

Statistics & Comments on the Results


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Tracking Indoor


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Simulate tracking Users trajectory in

indoor environment.

Red curvesimulates usersmotion.

Blue curvesimulatescalculatedtrajectory of userreceiver in EKFmodel.

Green curve

simulatescalculatedtrajectory of userreceiver withoutEKF.


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Errors in indoor positioning.

Red points:positioning errors

without EKF.

Green points:positioning errors

in EKF model.


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Statistics & Comments on the Results

Comments on simulation results:

The maximum error is about 0.5 meters in caseusing EKF model, whereas 3.5 meters in casewithout EKF.

Trajectory of user receiver in EKF model is notclosed to trajectory of users motion correlative withappreciably positioning error. However the errorreduces very quickly by exponential curve.

The average estimation error of EKF is very smallthan without EKF case. However, several points incurve are under suddenly changing errors

C l i


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EKF

Conclusion

Part 2Part 1

Part 3

-Activities-Noise sources- Problem solvings

C l i


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Conclusion


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Handover between outdoor-indoor environment

Future work


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Differential GPS (DGPS)

Reference

Station

.

C l i


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Conclusion

Part 2Part 1

EKF

Part 3


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Linear Kalman


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Error in Linear Kalman


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Modun WaveCom Fastrack Supreme 20


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2 2 2

1 1 1 1( ) ( ) ( )u u u u x x y y z z b

2 2 2

2 2 2 2( ) ( ) ( )

u u u u x x y y z z b

2 2 23 3 3 3( ) ( ) ( )u u u u x x y y z z b

2 2 2

4 4 4 4( ) ( ) ( )

u u u u x x y y z z b


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Cu trc Frame ca tn hiu GPS


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Thng k sai s (CA code)

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