Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.
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Δευτέρα 14 Δεκεμβρίου 2015
Sensors, Vol. 15, Pages 31464-31481: Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device
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Αλέξανδρος Γ. Σφακιανάκης Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,0030693260717...
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heory of COVID-19 pathogenesis Publication date: November 2020Source: Medical Hypotheses, Volume 144Author(s): Yuichiro J. Suzuki ScienceD...
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Alimentary Pharmacology &Therapeutics, EarlyView. https://ift.tt/2qECBIJ
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