Abstract
Increasingly, safety and liability critical applications require GNSS-like positioning metrics in environments where GNSS cannot work. Indoor navigation for the vision impaired and other mobility restricted individuals, emergency responders and asset tracking in buildings demand levels of positioning accuracy and integrity that cannot be satisfied by current indoor positioning technologies and techniques. This paper presents the challenges facing positioning technologies for indoor positioning and presents innovative algorithms and approaches that aim to enhance performance in these difficult environments. The overall aim is to achieve GNSS-like performance in terms of autonomous, global, infrastructure free, portable and cost efficient. Preliminary results from a real-world experimental campaign conducted as part of the joint FIG Working Group 5.5 and IAG Sub-commission 4.1 on multi-sensor systems, demonstrate performance improvements based on differential Wi-Fi (DWi-Fi) and cooperative positioning techniques. The techniques, experimental schema and initial results will be fully documented in this paper.
Highlights
This paper reports about an experiment, conducted in a GNSS-denied/challenged, indoor environment at the Queen Victoria Market (QVM) located in Melbourne, Australia
We developed a cooperative system comprising of seven pedestrian users equipped with smartphones using an integration of signals such as Wi-Fi (Wireless Fidelity) and Ultra-wide Band (UWB) (Ultra-Wide Band) with the objective of achieving precise positioning in indoor environments
This is represented by the regions where the UWB range measurements are relatively constant
Summary
This paper reports about an experiment, conducted in a GNSS-denied/challenged, indoor environment at the Queen Victoria Market (QVM) located in Melbourne, Australia. A Differential Wi-Fi (DWi-Fi) scheme by analogy to DGNSS is applied It is a network calibration method based on reference stations realized by low-cost Raspberry Pi units which is able to derive the correction parameters in real-time. In this way, the measured RSS values at the user’s side are corrected, the fingerprinting database is continuously updated, and an adaption to the possible changes in the dynamics of the environment is achieved. Concluding remarks and an outlook are drawn in the final section 8
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