Self-positioning of smartphones in indoor environments offers a wide variety of applications. Anyway, in harsh environments, the achievable accuracies using received signal strength indicator measurement data are comparably low. However, restrictions due to geometry allow more accurate estimates of smartphone positions and trajectories. Based on received signal strength data from Bluetooth low energy beacons and Gaussian assumptions, an application of a discrete-state hidden Markov model – taking the geometry into account – in combination with dynamic model parameter estimation, leads to a significant improvement of error statistics.
Geometry-Aided BLE-Based Smartphone Positioning for Indoor Location-Based Services
23.11.2020
3169196 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Towards an Efficient, Intelligent, Opportunistic Smartphone Indoor Positioning System
Online Contents | 2015
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