Indoor Localization Without the Pain

Krishna ChintalapudiAnand Padmanabha Iyer

Venkata N. Padmanabhan

——presented by Xu Jia-xing

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

Schemes that require specialized infrastructure. requires infrastructure deployment

Schemes that build RF signal maps. takes too much time

Model-Based Techniques. much less efforts than RF map; but still need a

lot of work to fit the models

Motivation-Related Work(1)

Localization in Indoor Robotics. requires special sensors and maps

Ad-Hoc localization. requires enough node density to enable multi-

hopping

Motivation-Related Work(2)

Can we do indoor localization without such pre-deployments

or limitations?

Works with existing WiFi infrastructure only

Does not require knowledge of Aps(placement, power,etc)

Even work with measurements by a single device

Does not require any explicit user participation

Motivation-EZ(1)

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.

Motivation-EZ(2)

Assumptions

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

Main idea of EZ-LDPL equations

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

Main idea of EZ

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

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)

Optimization-GA

Manner

Randomly pick Pi and ri with boundaries

Use the LDPL equation :if there are m APs and n locationsthen reduce from 4m+2n to 4m

Optimization-Reducing the Search Space

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.

Optimization-Reducing the Search Space

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.

Optimization-Reducing the Search Space

Calculate all equations fit R1

Randomly generate parameters of all equations fit R2 to R5

Calculate parameters of all unknown locations

Optimization-Reducing the Search Space

There are gain differences among different device.

Introduce an additional unkown parameter G

Optimization-Relative Gain Estimation Algorithm

Calculate △Gk1k2 is possible:◦ represent all RSS from a device with a vector

Optimization-Relative Gain Estimation Algorithm

If “Close”

Optimization-APSelect algorithm

Common Methods APSelect algorithm

Wide coverage

Low standard deviation in RSS

High average signal strength

Select each AP to provide information that other selected AP do not

1.Normalize pij into range(0,1)

2.Let

3.Cluster APs one by one by 入4.Select the AP which can be seen by most known locations.

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

Experiment-Performance

Experiment-Performance

Normal accuracy.

Experiment-Training Data

More training data greater accuracy.

Experiment-new mobile

Great performance. Different devices are better.

Experiment-Multiple devices training

The same as one device.

Experiment-APSelect and LocSelect

Great improvement.

Motivation Main idea of EZ Optimization Experiment Conclusion

Outline

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.

Conclusion