  Positioning systems and frameworks use various techniques to determine the
position of an object. Some of the existing solutions combine different sensory
data at the time of positioning in order to compute more accurate positions by
reducing the error introduced by the used individual positioning techniques. We
present OpenHPS, a generic hybrid positioning system implemented in TypeScript,
that can not only reduce the error during tracking by fusing different sensory
data based on different algorithms, but also also make use of combined tracking
techniques when calibrating or training the system. In addition to a detailed
discussion of the architecture, features and implementation of the extensible
open source OpenHPS framework, we illustrate the use of our solution in a
demonstrator application fusing different positioning techniques. While OpenHPS
offers a number of positioning techniques, future extensions might integrate
new positioning methods or algorithms and support additional levels of
abstraction including symbolic locations.
