OpenHPS: An Open Source Hybrid Positioning System
This system provides a flexible framework for developers and researchers working on indoor and outdoor positioning systems, offering a modular approach to integrate and combine different positioning techniques.
This paper introduces OpenHPS, an open-source hybrid positioning system implemented in TypeScript. It aims to reduce positioning error by fusing various sensory data and tracking techniques during both real-time tracking and system calibration/training.
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.