1. Project Description Positioning
Positioning of mobile users and devices in buildings enables many advanced applications: tracking
fire fighters, localizing heart patients, tracking psychiatric patients, locating security personnel and
night guards, retrieving critical and/or expensive equipment, active tagging of equipment, automated
tour guiding in museums.
By piggy-backing on existing sensor network infrastructure, two essential requirements for the
positioning of a mobile device or person are met: transmission or reception quality measurements
like receive signal strength indication (RSSI) can be used as the metric on which position can be
calculated, and the existing network infrastructure can be re-used, overcoming the need for new
dedicated infrastructure.
Due to the harsh multipath environment in indoor areas, classic positioning techniques using multi-
angulation or multi-lateration are not very attractive. Therefore, more sophisticated probabilistic
methods need to be used to transform noisy measurements into position information. In these
probabilistic methods, the user’s location is determined by comparing the observed measurement
values to a known set of values.
Determining the known set of values for a specific building is also known as ‘fingerprinting’. It
consists of ‘offline’ and ‘run-time’ stages. In the offline stage, the measurement of the RF features at
known user locations is carried out. Wireless access points deployed in the environment periodically
transmit beacons. A signal metric measured from detected beacons, such as signal strength or
multipath power delay profile, can be a useful RF feature. The collected signal metrics (location
fingerprints) are stored in a location database which relates the signal information and the
coordinates of the known locations. In the run-time stage, measurement of the same signal metric as
used in the offline stage is carried out. The location database is accessed to match the signal metrics
collected during the run-time (at an unknown location) with the stored entries. A location algorithm
is then applied to estimate the location.
As fingerprinting is a costly phase which needs to be carried out regularly in order to compensate for
structural changes in the building, the real challenge in this WBA project is in replacing the location
database by an empirical propagation model that probabilistically captures the dependence of the
signal strength on distance, in a way that is independent on the type of building.
Objects’ or persons’ position estimates can also be made more accurate by comparing estimates at
different moments in time, and applying a filter (Kalman filter) to consecutive calculation results of
the probabilistic location algorithm.
In the WBA project, research and development is done in all of the above mentioned domains, to
come to a self-contained proof-of-concept system which is seamlessly integrated with the other
parts of the project. It makes use of the SANET (Sensor & Actuator Network) infrastructure and
accesses data through the gateway and the high data-rate wireless mesh network.
Measurements in different types of buildings are used to build a generic statistical model. The
advanced probabilistic methods applied in the positioning engine finds the best possible position
estimate, while calculation speed still enables real-time applications and overhead of the retrieval of
RF measurements on the SANET protocol is kept to a minimum, enabling this technique to be used
on battery-operated and resource-constrained sensor devices.