Wba2 Project Description En Tech
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Positioning mobile users and devices in buildings enables applications like tracking first responders, patients, and security personnel. Existing sensor network infrastructure can be used to measure metrics like signal strength for positioning by reusing the network instead of new dedicated infrastructure. More sophisticated probabilistic methods are needed to transform noisy indoor measurements into positions by comparing observed values to a known fingerprint database of location-specific signal metrics. The project aims to develop a self-contained proof-of-concept positioning system that replaces the fingerprint database with a statistical model of signal strength dependence on distance and filters position estimates over time for increased accuracy.
Wba2 Project Description En Tech
- 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.