Acquiring traces from random walks
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Acquiring traces from random walks. Project final presentation By: Yaniv Sabo Aviad Hasnis Supervisor: Daniel Vainsencher. Project Goal. Creating traces of indoor walks using signals collected by different agents. Possible problem: GPS is not feasible indoors. Possible Solution.

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Acquiring traces from random walks

Acquiring traces from random walks

Project final presentation

By:

Yaniv Sabo

AviadHasnis

Supervisor:

Daniel Vainsencher


Project goal
Project Goal

  • Creating traces of indoor walks using signals collected by different agents.

  • Possible problem: GPS is not feasible indoors.


Possible solution
Possible Solution

  • The agents will be cellular devices (HTC) using Android platform.

  • Using the following signals:

    • Built-in accelerometer producing the device acceleration in each direction.

    • Built-in magnetometer measuring the magnetic field at the device’s location.

    • WIFI signal levels received from multiple routers, combined using triangulation.


Background
Background

  • Android is a mobile operating system currently developed by Google.

  • Accelerometer

  • Magnetometer

  • WIFI

  • Triangulation



Possible solution cont d
Possible Solution – Cont’d

  • Each one of the signals we used isn’t accurate enough on it’s own.

    • The direction received from the Accelerometer accumulates error very fast.

    • The WiFi measurements have a high variance because of significant noise.

  • Integrating the former inputs in order to create an accurate path.


Wifi signal strength to distance
WiFi signal strength to distance

  • We wanted to find a function that converts the WiFi signal level received to the distance from the AP.

  • According to articles, the function should behave like:

    where a, b and c are constants.

  • We did some experiments and using Least Square Error we found that these constants should be:


System flow
System Flow

Accelerometer

WIFI receiver

Magnetometer



Algorithm cont d
Algorithm - Cont’d

  • WiFi regularization

    • Limiting the velocity to normal walking speed and limiting the acceleration.


Algorithm cont d1
Algorithm - Cont’d

  • Combining WiFimeasurements

    • Measurements are received from different WiFi APs at different times.

    • Requires an algorithm to combine these signals.

    • Two approaches: ring and a circle.


Algorithm cont d2
Algorithm - Cont’d

  • Combining Accelerometer data with Magnetometer data

    • The Accelerometer accumulates error in a high rate.

    • We use the Magnetometer to get the device’s direction and the Accelerometer to get the device’s acceleration.


Algorithm cont d3
Algorithm - Cont’d

  • After we have the WiFi processed measurements as well as the Accelerometer and Magnetometer data combined we integrate these signals.

  • We use loss functions to denote the distance of the solution from the signals and we attempt to minimize these loss functions.

  • We also perform regularization on the solution.


Algorithm cont d4
Algorithm - Cont’d

  • We tried different loss functions for the WiFi measurements:

  • We also tried different loss functions for the Accelerometer and Magnetometer measurements:




Some results cont d1
Some Results – Cont’d


Conclusions
Conclusions

  • Each one of the signals we used isn’t accurate enough on it’s own. We combined these signals to get a more accurate solution.

  • We have found that the quality of the solution depends heavily on the loss function used.

  • The methods used to collect data from the device have great effect on the precision of this data.


Future work
Future Work

  • Investigating additional loss functions, as well as faster functions for unconstrained optimization than MATLAB’s fminunc.

  • Using additional signals, such as Bluetooth, to improve the solution.

  • Combining different traces to build a map of the interior of a building.


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