1. RF-based positioning

2. RF-based systems
Existing wireless LANs can be leveraged for indoor positioning
Two classes of WLAN location systems:
Client-based
Infrastructure-based
In a client-based system
APs transmit frames
Clients use signal strength of AP-transmitted frames to infer location

3. Phases of RF-based location system
Offline training phase
Record the strength of signals received from Aps at selected locations
Build a radio map
Online location determination phase
Take signal strength samples at the client from APs
Search the radio map to estimate user location

4. Deterministic fingerprinting approach
Signal strength at each location is represented by a scalar value (e.g. mean value
Deterministic approaches are used to estimate user location
Example: Radar system [Infocom’00] (it uses the nearest neighborhood technique)

5. Probabilistic fingerprinting approach
Signal strength at each location is represented by a probability distribution
Probabilistic approaches are used to estimate user location
Example: HORUS system (Mobisys’05)

6. Wireless channel variation Temporal
Temporal variation
Normalized histogram of the received signal strength from one AP over a period of 5 mins
Fig. 1 in (Mobisys’05)

7. Wireless channel variation Temporal
Temporal variation
Autocorrelation function of the samples collected from one AP at a fixed position.
Do not assume independence when sampling from the same AP!
Fig. 2 in (Mobisys’05)

8. Wireless channel variation Signal strength & link quality
Monotonically increasing function between the average signal strength and number of samples received from an AP.
Fig. 3 in (Mobisys’05)

9. Wireless channel variation Spatial
Figure 4 shows the average signal strength received from an access point as the distance from it increases. The sig- nal strength varies over a long distance due to attenuation of the RF signal.
Large-scale variations are desirable in RF-based sys- tems as they lead to changing the signature stored in the radio map for different locations and, hence, better dif- ferentiation between these locations.
These variations happen when the user moves over a small distance (order of wavelength). This leads to changes in the average received signal strength. For the b networks working at the 2.4 GHz range, the wavelength is 12.5 cm and we measure a variation in the average signal strength up to 10 dBm in a distance as small as 7.6 cm (3 inches) (Figure 5).
Dealing with small-scale variations is challenging. To limit the radio map size and the time required to build the radio map, selected radio map locations are typically placed more than a meter apart. This means that the ra- dio map does not capture small-scale variations leading to decreased accuracy in the current WLAN location sys- tems.
HORUS [Mobisys’05]

10. Localisation problem Assumption
At each 2D location x (in discrete or continuous location space X), we get signal strengths from k access points
Problem
Given a signal strength vector s=(s1,…,sk), find the location x that maximizes the probability P(x/s)

11. HORUS: offline phase Map Builder
During the offline phase, Horus estimates the signal strength histogram for each access point at each location
It then uses an autoregressive model to capture the correlations between different samples from the same AP

12. HORUS: offline phase Map Builder
The signal strength histogram is approximated by a Gaussian with mean and variance
If we represent the signal strength time series as an autoregressive model
the distribution of the average of n correlated samples is a Gaussian with mean
and variance
The model in Equation 4 states that the current sig- nal strength value (st) is a linear aggregate of the previ- ous signal strength value (st−1) and an independent noise value (vt). The parameter α gives flexibility to the model as it can be used to determine the degree of autocorrela- tion of the original process. For example, if α is zero, the samples of the process st are i.i.d.’s, whereas if α is 1 the original samples are identical (autocorrelation=1).
parameters stored
in the radio map

13. HORUS: online phase Discrete Space Estimator
Average the value of n consecutive samples to get vector s
Then search for the location of maximum probability given s
using radio map to compute P(s/x) as follows:
This returns a single location from the set of locations in the radio map

14. HORUS: online phase Continuous Space Estimator
Centre of mass technique
Obtain the centre of mass of the
top N most likely locations)
Time averaging technique
Use a time window of size W
Average the last W location estimates obtained by
the discrete-space or continuous-space estimator

15. HORUS: online phase Small-Scale Compensator
The radio map does not capture small scale variations
How to detect small-scale variations?
Exploit the fact that users’ locations cannot change too fast
How to compensate for small-scale variations?
Slightly perturb the signal strength observations from each of k access points
Select the perturbed combination that is the nearest to the previous user location

16. Summary
HORUS is an example of radio-based fingerprinting method used for indoor positioning
Offline phase: used to build radio map
Online phase: used to estimate position given radio map
Two flavours:
Discrete
Continuous
Two optimisations:
Exploit temporal correlations in signal strength observations
Compensate for small scale signal strength variations

17. Related reading
M. Youssef and A. Agrawala. The Horus WLAN location determination system. In Proc. Of the 3rd Intl Conf on mobile systems, application and services, 2005.