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Teodoro, M. F., Alves, J. V. & Andrade, M. A. P. (2024). Modeling meteorological data by Kalman filter approach. In  Jose Machado, Filomena Soares, Justyna Trojanowska, Vitalii Ivanov, Katarzyna Antosz, Dagmar Cagáňová, Vijaya Kumar Manupati, Alejandro Pereira (Ed.), Innovations in Industrial Engineering III, Conference proceedings. (pp. 370-380). Povoação, São Miguel: Springer.

M. F. Teodoro et al.,  "Modeling meteorological data by Kalman filter approach", in Innovations in Industrial Engineering III, Conf. proceedings,  Jose Machado, Filomena Soares, Justyna Trojanowska, Vitalii Ivanov, Katarzyna Antosz, Dagmar Cagáňová, Vijaya Kumar Manupati, Alejandro Pereira, Ed., Povoação, São Miguel, Springer, 2024, pp. 370-380

@inproceedings{teodoro2024_1766222676661,
	author = "Teodoro, M. F. and Alves, J. V. and Andrade, M. A. P.",
	title = "Modeling meteorological data by Kalman filter approach",
	booktitle = "Innovations in Industrial Engineering III, Conference proceedings",
	year = "2024",
	editor = " Jose Machado, Filomena Soares, Justyna Trojanowska, Vitalii Ivanov, Katarzyna Antosz, Dagmar Cagáňová, Vijaya Kumar Manupati, Alejandro Pereira",
	volume = "",
	number = "",
	series = "",
	doi = "10.1007/978-3-031-61582-5_31",
	pages = "370-380",
	publisher = "Springer",
	address = "Povoação, São Miguel",
	organization = "",
	url = "https://link.springer.com/chapter/10.1007/978-3-031-61582-5_31"
}

TY  - CPAPER
TI  - Modeling meteorological data by Kalman filter approach
T2  - Innovations in Industrial Engineering III, Conference proceedings
AU  - Teodoro, M. F.
AU  - Alves, J. V.
AU  - Andrade, M. A. P.
PY  - 2024
SP  - 370-380
SN  - 2195-4356
DO  - 10.1007/978-3-031-61582-5_31
CY  - Povoação, São Miguel
UR  - https://link.springer.com/chapter/10.1007/978-3-031-61582-5_31
AB  - The studied problem falls within Navigation and Integration Systems, consisting of the estimation of temperature values obtained through multiparametric buoys anchored at sea (or PCD – Data Collection Platforms) that send their temperature data (among others) in interaction with some satellites. This data, in turn, is sent by the same satellites to reception and analysis stations located on the ground. The authors proposed the implementation of a Kalman Filter to filter the read a noisy signal. With the state variable identified (temperature), obtaining the state and measurement equations constitute the starting point for implementing the estimation method. The Kalman Filter algorithm model had and excellent performance and it can be, in practice, a very reliable and recommended solution for studying data from sensors of this type.
ER  -