Indoor Localization Without the Pain

Report
Krishna Chintalapudi
Anand Padmanabha Iyer
Venkata N. Padmanabhan
——presented by Xu Jia-xing
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
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Schemes that require specialized
infrastructure.
 requires infrastructure deployment
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Schemes that build RF signal maps.
 takes too much time
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Model-Based Techniques.
 much less efforts than RF map; but still need a
lot of work to fit the models
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Localization in Indoor Robotics.
 requires special sensors and maps
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Ad-Hoc localization.
 requires enough node density to enable multihopping
Can we do indoor localization
without such pre-deployments
or limitations?
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Works with existing WiFi infrastructure only
Does not require knowledge of Aps(placement,
power,etc)
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Even work with measurements by a single device
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Does not require any explicit user participation
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There are enough WiFi APs to provide excellent
coverage throughout the indoor environment
Users carry mobile devices, such as smartphones
and netbooks, equipped with WiFi
Occasionally a mobile device obtains an absolute
location fix, say by obtaining a GPS lock at the
edges of the indoor environment, such as at the
entrance or near a window.
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
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xj: the jth location
ci: the ith AP’s location
Pi: the power of the ith AP
pij: the RSS received by mobile in the jth
location form the ith AP
ri: the rate of fall of RSS in the vicinity of the
ith AP
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
Manner
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10% of the solutions with the highest fitness are
retained.
10% of the solutions are randomly generated.
60% of the solutions are generated by crossover.
The remaining 20% solutions are generated by
randomly picking a solution from the previous
generation and perturbing it(Only Pi and ri)
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Randomly pick Pi and ri with boundaries
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Use the LDPL equation :
if there are m APs and n locations
then reduce from 4m+2n to 4m
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R1 : If an AP can be seen from five or more
fixed (or determined)locations, then all four
of its parameters can be uniquely solved.
R2 : If an AP can be seen from four fixed
locations, there exist only two possible
solutions for the four parameters of the AP.
R3 : If an AP can be seen from three fixed
locations, randomly pick ri, there exist only
two possible solutions for the three
parameters of the AP.
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R4 : If an AP can be seen from two fixed
locations, randomly pick Pi and ri, there exist
only two possible solutions for the two
parameters of the AP.
R5 : If an AP can be seen from one fixed
location, randomly pick all parameters.
R6 : If the parameters for three (or more) APs
have been fixed, then all unknown locations
that see all these APs can be exactly
determined using trilateration.
Calculate all equations fit R1
Randomly generate parameters
of all equations fit R2 to R5
Calculate parameters of all
unknown locations
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There are gain differences among different
device.
Introduce an additional unkown parameter G
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Calculate △Gk1k2 is possible:
◦ represent all RSS from a device with a vector
If “Close”
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Wide coverage
Low standard
2.Letdeviation in RSS
1.Normalize pij into range(0,1)
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Select each AP to
provide information
that other selected
AP do not
3.Cluster APs one by one by 入
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High average signal
4.Select the AP which can be seen
by most
known locations.
strength
Common Methods
APSelect algorithm
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
Normal accuracy.
More training data greater accuracy.
Great performance. Different devices are better.
The same as one device.
Great improvement.
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Motivation
Main idea of EZ
Optimization
Experiment
Conclusion
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The idea is good. It’s different from
traditional methods.
The optimization is functional.
The LDPL Model is not perfect.
Does not mention how to refresh the RSS
Model.

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