Indoor Localization Without the Pain

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Transcript Indoor Localization Without the Pain

Krishna Chintalapudi Anand Padmanabha Iyer Venkata N. Padmanabhan

——presented by Xu Jia-xing

     Motivation Main idea of EZ Optimization Experiment Conclusion

     Motivation Main idea of EZ Optimization Experiment Conclusion

   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

  Localization in Indoor Robotics.

 requires special sensors and maps Ad-Hoc localization.

 requires enough node density to enable multi hopping 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

 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 Main idea of EZ Optimization Experiment Conclusion

     x j : the j th c i : the i th location AP’s location P i : the power of the i th AP p ij : the RSS received by mobile in the j th location form the i th AP r i : the rate of fall of RSS in the vicinity of the i th AP

     Motivation Main idea of EZ Optimization Experiment Conclusion

Manner    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 P i and r i )

 Randomly pick Pi and ri with boundaries  Use the LDPL then reduce equation : if there are m APs and n locations from 4m+2n to 4m

   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 r parameters of the AP.

i , there exist only two possible solutions for the three

   R4 : If an AP can be seen from two fixed locations, randomly pick P i parameters of the AP.

and r i , there exist only two possible solutions for the two 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

 There are gain differences among different device.

 Introduce an additional unkown parameter G

 ◦ Calculate △ Gk 1 k 2 is possible: represent all RSS from a device with a vector If “Close”

 Wide coverage  ij 2.Let

Low standard deviation in RSS 3.Cluster APs one by one by 入  High average signal strength  Select each AP to provide information that other selected AP do not Common Methods APSelect algorithm

     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

  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.