RSSI Filtering Methods Applied to Localization using Bluetooth Low Energy
Aditya Kaduskar1, Omkar Vengurlekar2, Varunraj Shinde3
1Aditya Kaduskar, Department of Electronics and Telecommunication‟s, PVG‟s College of Engineering, Pune, India.
2Omkar Vengurlekar, Department of Electronics and Telecommunication‟s, PVG‟s College of Engineering, Pune, India.
3Varunraj Shinde, Department of Electronics and Telecommunication‟s, PVG‟s College of Engineering, Pune, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 290-299 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4413099320 | DOI: 10.35940/ijrte.C4413.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Bluetooth Low Energy or BLE is a technology targeting mostly small-scale IoT applications including wearables and broadcasting beacons that require devices to send small amounts of data using minimal power. This paper focuses on our implementation, which is a system, designed to filter RSSI (Received Signal Strength Indicator), calculate the co-ordinates of a BLE device that is programmed as a Beacon and display the coordinates. Since RSSI is susceptible to noise and a downgrade in its reliability is unavoidable, several filtration methods have been used. The ‘Kalman – Histogram’ method, which incorporates the usage of a histogram of the RSSI readings along with the Kalman filter, is our own approach to tackle issues regarding noisy RSSI readings. The localization of stationary ‘Assets’, has been evaluated using the Trilateration algorithm: a result in mathematics which is used to locate a single point using its distance from three or more other points. The purpose of this research work is to provide a comparative result analysis of the results obtained using the aforementioned filters, indicating the effect of these filters on our localization system. As our research suggests, the ‘Kalman – Histogram’ filter performs better as compared to other filters and can be used in localization applications for better accuracy.
Keywords: Bluetooth Low Energy (BLE), Raspberry Pi, Localization.