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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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