Hybrid Localization Framework Supporting Multiple Standards and Manifold Post-Processing


Nowadays much research activities are devoted to provide hybrid localization. Software written for systems based on a single standard is usually highly customized due to the differences of the underlying technologies. The reasons can already be seen by comparing the input and output parameters. However, the limitation to only a few underlying standards leads also to a high degree of specialization for hybrid systems. Another problem is time-to-market, as a result of which software is developed rather quickly. Both results in software, which is neither variable nor extensible. To take into account these problems, this paper proposes a framework for hybrid localization. After identifying drawbacks of related approaches, requirements are compiled. In contrast to most publications, this paper puts special emphasis on implementation details. As a novelty, our framework is based on operators, which enables to treat generation and processing of data in an equal manner. Hereby, many real world problems, like coordinate transformations, can be solved naturally. Moreover, our framework is capable of dealing with arbitrary input and output parameters and it supports push and pull behaviour. We test our solution by applying diverse received signal strength-based algorithms. Experiments and simulations are performed to show the potential of the framework and its broad application.


A Hybrid Localization Framework Supporting Multiple Standards and Manifold Post-Processing, Marco Gunia, Bo Zhang, Niko Joram and Frank Ellinger, in proceeding of ICL-GNSS 2016, Barcelona, Spain, June 2016.