Installation guide

17
Wi-Fi Location-Based Services—Design and Deployment Considerations
OL-11612-01
Location Tracking Approaches
Operational Phase
In the operational phase, a group of receiving sensors provide signal strength measurements pertaining
to a tracked mobile device (network-side reporting implementation) and forwards that information to a
location tracking server. The location server uses a complex positioning algorithm and the radio map
database to estimate the location of the mobile device. The server then reports the location estimate to
the location client application requesting the positioning information.
Location patterning positioning algorithms can be classified into three basic groups:
• Deterministic algorithms attempt to find minimum statistical signal distance between a detected
RSSI location vector and the location vectors of the various calibration sample points. This may or
may not be equal to the minimum physical distance between the actual device physical location and
the recorded location of the calibration sample. The sample point with the minimum statistical
signal distance between itself and the detected location vector is generally regarded as the best raw
location estimate contained in the calibration database. Examples of deterministic algorithms are
those based on the computation of Euclidean, Manhattan, or Mahalanobis distances.
• Probabilistic algorithms use probability inferences to determine the likelihood of a particular
location given that a particular location vector array has already been detected. The calibration
database itself is considered as an a priori conditional probability distribution by the algorithm to
determine the likelihood of a particular location occurrence. Examples of such approaches include
those using Bayesian probability inferences.
• Other techniques go outside the boundaries of deterministic and probabilistic approaches. One such
approach involves the assumption that location patterning is far too complex to be analyzed
mathematically and requires the application of non-linear discriminant functions for classification
(neural networks). Another technique, known as support vector modeling or SVM, is based on risk
minimization and combines statistics, machine learning, and the principles of neural networks.
To gain insight into how such location patterning algorithms operate, a very simple example is provided
of the use of a deterministic algorithm, the Euclidean distance. As stated earlier, deterministic algorithms
compute the minimum statistical signal distance, which may or may not be equal to the minimum
physical distance between the actual device physical location and the recorded location of the calibration
sample.
For example, assume two access points X and Y and a mobile device Z. Access point X reports mobile
device Z with an RSS sample of x
1
. Almost simultaneously, access point Y reports mobile device Z with
an RSS sample of y
1
. These two RSS reports can be represented as location vector of (x
1
,y
1
). Assume
that during the calibration phase, a large population of location vectors of the format F(x
2
,y
2
) were
populated into the location server calibration database, where F represents the actual physical
coordinates of the recorded location. The location server can calculate the Euclidean distance d between
the currently reported location vector (x
1
,y
1
) and each location vector in the calibration radio map as
follows:
The physical coordinates F associated with the database location vector possessing the minimum
Euclidean distance from the reported location vector of the mobile device is generally regarded as being
the correct estimate of the position of the mobile device.
In a similar fashion to RSS lateration solutions, real-time location systems using location patterning
typically allow vendors to make good use of existing wireless infrastructure. This can often be an
advantage over AoA, ToA, and TDoA approaches, depending on the particular implementation. Location
patterning solutions are capable of providing very good performance in indoor environments, with a
2
12
2
12
)y(y)x(xd
−+−=