Bagging to Improve the Calibration of RSSI Signals in Bluetooth Low Energy (BLE) Indoor Distance Estimation
| Autor: | Giuseppe Salvi, Salvatore Gaglione, Antonio Maratea |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Bootstrap aggregating ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Transmitter Real-time computing Nonparametric statistics 02 engineering and technology 01 natural sciences law.invention Beacon Bluetooth law Proximity sensor Computer Science::Networking and Internet Architecture 0202 electrical engineering electronic engineering information engineering Calibration 020201 artificial intelligence & image processing Wireless sensor network 0105 earth and related environmental sciences |
| Zdroj: | SITIS |
| Popis: | Originally conceived as proximity sensors, smart Bluetooth (Bluetooth Low Energy or BLE) beacons have been quickly adopted as inexpensive means to estimate distance of the transmitter from the receiver. Unfortunately the Received Signal Strength in unstable and produces such oscillations that right beyond a couple of meters the accurate estimation of distances becomes extremely challenging. In this paper, starting from a preprocessed RSSI vector of measurements, a Bootstrap Aggregating procedure is proposed to improve the calibration of RSSI signals. The proposed method, in combination with robust and non parametric statistics, reaches a sub-meter precision up to 6 meters of distance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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