Adaptive Sequential Monte Carlo Filter for Indoor Positioning and Tracking With Bluetooth Low Energy Beacons

Adaptive Sequential Monte Carlo Filter for Indoor Positioning and Tracking With Bluetooth Low Energy Beacons

We model the tracking of Bluetooth low-energy (BLE) transmitters as a three layer hidden Markov model with joint state and parameter estimation. We are after a filtering distribution by Bayesian approximation using Monte Carlo sampling techniques. In a test environment decorated with multiple BLE se...

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Bibliographic Details
Main Authors: F. Serhan Danis, A. Taylan Cemgil, Cem Ersoy
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Bluetooth low-energy
indoor positioning and tracking
parameter estimation
sequential Monte Carlo
wasserstein interpolation
Online Access:https://ieeexplore.ieee.org/document/9366479/

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