Acquiring traces from random walks

1. Acquiring traces from random walks Project final presentation By: Yaniv Sabo AviadHasnis Supervisor: Daniel Vainsencher

2. Project Goal • Creating traces of indoor walks using signals collected by different agents. • Possible problem: GPS is not feasible indoors.

3. 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.

4. Background • Android is a mobile operating system currently developed by Google. • Accelerometer • Magnetometer • WIFI • Triangulation

5. Background - Cont’d

6. 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.

7. 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:

8. System Flow Accelerometer WIFI receiver Magnetometer

9. Algorithm 3

10. Algorithm - Cont’d • WiFi regularization • Limiting the velocity to normal walking speed and limiting the acceleration.

11. 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.

12. 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.

13. 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.

14. Algorithm - Cont’d • We tried different loss functions for the WiFi measurements: • We also tried different loss functions for the Accelerometer and Magnetometer measurements:

15. Some Results

16. Some Results – Cont’d

17. Some Results – Cont’d

18. 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.

19. 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.