Low-Cost Indoor Localization System Combining Multilateration and Kalman Filter
| Autor: | Leonardo Sestrem De Oliveira, Ohara Kerusauskas Rayel, Paulo Leitão |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Internet of things
Computer science business.industry Multilateration Real-time computing Location awareness Robotics Energy consumption Kalman filter computer.software_genre Bluetooth low energy Indoor positioning system Position (vector) Face (geometry) Artificial intelligence Electronics business Kalman filtering computer |
| Zdroj: | ISIE |
| DOI: | 10.1109/isie45552.2021.9576353 |
| Popis: | Indoor localization systems play an important role to track objects during their life-cycle in indoor environments, e.g., related to retail, logistics and mobile robotics. These positioning systems use several techniques and technologies to estimate the position of each object, and face several requirements such as position accuracy, security, range of coverage, energy consumption and cost. This paper describes a practical implementation of a BLE (Bluetooth Low Energy) based localization system that combines multilateration and Kalman filter techniques to achieve a low cost solution, maintaining a good position accuracy. The proposed approach was experimentally tested in an indoor environment, with the achieved results showing a clear low cost system presenting an increase of the estimated position accuracy by 10% for an average error of 2.33 meters This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UIDB/05757/2020. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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