240 research outputs found
Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting
In this paper, we propose hybrid building/floor classification and
floor-level two-dimensional location coordinates regression using a
single-input and multi-output (SIMO) deep neural network (DNN) for large-scale
indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits
the different nature of the estimation of building/floor and floor-level
location coordinates and uses a different estimation framework for each task
with a dedicated output and hidden layers enabled by SIMO DNN architecture. We
carry out preliminary evaluation of the performance of the hybrid floor
classification and floor-level two-dimensional location coordinates regression
using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere
University of Technology (TUT), Finland, covering a single building with five
floors. Experimental results demonstrate that the proposed SIMO-DNN-based
hybrid classification/regression scheme outperforms existing schemes in terms
of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and
AI (GCA'18
Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings
POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe
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