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Acquiring traces from random walksPowerPoint Presentation

Acquiring traces from random walks

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## PowerPoint Slideshow about ' Acquiring traces from random walks' - jakeem-mooney

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### Acquiring traces from random walks

Project final presentation

By:

Yaniv Sabo

AviadHasnis

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

- 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

- Android is a mobile operating system currently developed by Google.
- Accelerometer
- Magnetometer
- WIFI
- Triangulation

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

- 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:

Algorithm - Cont’d

- WiFi regularization
- Limiting the velocity to normal walking speed and limiting the acceleration.

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’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’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’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’d

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

- 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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