1. Dejavu:An accurate Energy-Efficient Outdoor Localization System
SIGSPATIAL '13

2. Dead Reckoning

3. Definition
Dead Reckoning is the process of calculating one's current position by using a previously determined position (fix), and advancing that position based upon known or estimated speeds over elapsed time and course.
Steps:
Given initial point Ci
Estimate Displacement to be travelled (using accelerometer)
Estimate azimuth (using compass/gyroscope)
Predict next point Ci+1, Vincenty’s Formula is used to calculate the geodesic coordinate based on the azimuth and distance C2 Θ1 C0
How to estimate distance and angle when using smartphones? C1

4. Estimating Displacement and Angle
Use gyroscope/compass data to calculate azimuth.
Displacement Estimation
Collect acceleration data for time t.
Double integrate acceleration data to estimate displacement. The following steps are used to convert the acceleration data to achieve the displacement values:
The acceleration signals are filtered [High pass filter]
Integrate the acceleration to obtain the velocity
The velocity signals are filtered [High pass filter]
Finally, the filtered signals of velocity is integrated to get the displacement signals.

5. Displacement Estimation Example
(initial velocity of the object is taken as 0) n
Acceleration (an)
Velocity (vn)
Displacement (xn) 1 2 3 4 5 6 7 8 9
Each sample is of size 10 and data is collected every second.
Estimated displacement for first sample

6. Example At time 09:00 displacement is estimated as 10 and angle 90°

7. Dejavu Outdoor localization system
Provides accurate and energy-efficient outdoor localization
Uses only energy-efficient sensors or sensors which are already running
leverages road landmarks like moving over potholes, bumps, tunnels etc

8. Introduction
GPS considered as de facto standard for outdoor localization
but it is energy hungry and accuracy limited in areas like tunnels
Dejavu's approach is based on low-energy sensors (accelerometer,gyroscope,compass)
Using array of sensors it identifies landmarks (anchors)
crowdsourcing to build database

9. System overview
Dead Reckoning

10. Raw sensor information
System collects raw sensor information
cellular network information (RSS and associated cell tower ID)
opprtunistically leverags the Wifi chip to collect surrounding Wifi APs

11. Error Resetting
uses linear accelaration combined with direction of motion to compute displacement
uses Vincenty's formula
to limit accumulated error system uses physical and virtual anchors

12. Physical and virtual anchors
Dejavu uses two types of anchors
physical anchors mapped to road features like bridges, tunnels, speed-bumps
extracted from map or through prior knowledge
virtual anchors detected automatically
includes points with unique GSM or Wifi RSS
learned through crowd-sourcing

13. Acc

14. Anchor detection
large number of road features can be identified on their unique signature

16. Physical anchors used to seed the anchor database
identified from maps or by prior knowledge of the location
different classes of physical anchors

18. Virtual anchors
uses un-supervised learning techniques to identify virtual anchors
anomaly detection techniques are used to identify anomalies in sensor readings.
they are clustered into sensor space to identify candidate clusters
points of each cluster are spatially clustered to identify the location of each anchor

19. Feature selection
Cellular and Wifi anchors correspond to points in RSS signal space with unique signature

20. Anomaly detection
h - bandwidth
n - sample size
K - kernel function

21. Two stage clustering
Cluster feature space using hierarchical clustering in vector feature space
Clustering will group similar anomalies
spatial clustering of points in clusters obtained previously
cluster is accepted if the number of points is above a threshold

22. Anomaly detection Geographical Space Time Hi-dimensional Sensor Space
Landmarks
Sensors
K-Means Clustering

23. Computing anchor location

24. Discussion Anchors aliasing Efficient matching Processing location
Other sensors
Handling heterogeneity

25. Evaluation

26. Virtual anchor detection accuracy

27. Anchor localization accuracy

28. Effect of anchor density on accuracy

29. Dejavu against other systems