Location-dependent information extraction for positioning

This paper presents an overview of current research investigations within the WHERE-2 Project with respect to location-dependent information extraction and how this information can be used towards the benefit of positioning. It is split into two main sections; the first one relies on non-radio means such as inertial sensors and prior knowledge about the environment geometry, which can be used in the form of map constraints to improve user positioning precision in indoor environments. The second section presents how location-specific radio information can be exploited in a more sophisticated way into advanced positioning algorithms. The intended solutions include exploitation of the slow fading dynamics in addition to the fast-fading parameters, adaptation of the system to its environment on both network and terminal sides and also how specific environmental properties such as the dielectric wall parameters can be extracted and thereafter used for more accurate fingerprinting database generation using Ray Tracing modelling methods. Most of the techniques presented herein rely on real-life measurements or experiments

PINSPOT: An oPen platform for INtelligent context-baSed Indoor POsiTioning

This work proposes PINSPOT; an open-access platform for collecting and sharing of context, algorithms and results in the cutting-edge area of indoor positioning. It is envisioned that this framework will become reference point for knowledge exchange which will bring the research community even closer and potentially enhance collaboration towards more effective and efficient creation of indoor positioning-related knowledge and innovation. Specifically, this platform facilitates the collection of sensor data useful for indoor positioning experimentation, the development of novel, self-learning, indoor positioning algorithms, as well as the enhancement and testing of existing ones and the dissemination and sharing of the proposed algorithms along with their configuration, the data used, and with their results

An Open Platform for Studying and Testing Context-Aware Indoor Positioning Algorithms

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, our proposal features novelties related to the collection and use of additional context data. The platform is realized in the form of a mobile client, currently implemented on Android. It enables manual collection of radio-maps—i.e. fingerprints of Wi-Fi signals—while also allowing for amending the fingerprints with various context data which could help improve the accuracy of positioning algorithms. While this is a research-in-progress platform, an initial experiment was carried out and its results were used to justify its applicability and relevance