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Article

Estimation of Displacement for Internet of Things Applications with Kalman Filter

Dipartimento di Automatica e Informatica, Politecnico di Torino, 10129 Torino TO, Italy
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Electronics 2019, 8(9), 985; https://doi.org/10.3390/electronics8090985
Received: 7 August 2019 / Revised: 22 August 2019 / Accepted: 23 August 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Embedded Devices in IoT)
In the vision of the Internet of Things, an object embedded in the physical world is recognizable and becomes smart by communicating data about itself and by accessing aggregate information from other devices. One of the most useful types of information for interactions among objects regards their movement. Mobile devices can infer their position by exploiting an embedded accelerometer. However, the double integration of the acceleration may not guarantee a reliable estimation of the displacement of the device (i.e., the difference in the new location). In fact, noise and measurement errors dramatically affect the assessment. This paper investigates the benefits and drawbacks of the use of the Kalman filter as a correction technique to achieve more precise estimation of displacement. The approach is evaluated with two accelerometers embedded in commercial devices: A smartphone and a sensor platform. The results show that the technique based on the Kalman filter dramatically reduces the percentage error, in comparison to the assessment made by double integration of the acceleration data; in particular, the precision is improved by up to 72%. At the same time, the computational overhead due to the Kalman filter can be assumed to be negligible in almost all application scenarios. View Full-Text
Keywords: accelerometer; Kalman filter; position tracking; displacement accelerometer; Kalman filter; position tracking; displacement
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MDPI and ACS Style

Ferrero, R.; Gandino, F.; Hemmatpour, M. Estimation of Displacement for Internet of Things Applications with Kalman Filter. Electronics 2019, 8, 985. https://doi.org/10.3390/electronics8090985

AMA Style

Ferrero R, Gandino F, Hemmatpour M. Estimation of Displacement for Internet of Things Applications with Kalman Filter. Electronics. 2019; 8(9):985. https://doi.org/10.3390/electronics8090985

Chicago/Turabian Style

Ferrero, Renato, Filippo Gandino, and Masoud Hemmatpour. 2019. "Estimation of Displacement for Internet of Things Applications with Kalman Filter" Electronics 8, no. 9: 985. https://doi.org/10.3390/electronics8090985

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