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M. Filomena Teodoro, José V. Alves & Andrade, M. A. P. (2024). Modeling Metereological Data by Kalman Filter Approach. International Conference Innovation in Engineering.

M. F. Teodoro et al.,  "Modeling Metereological Data by Kalman Filter Approach", in Int. Conf. Innovation in Engineering, São Miguel Island – Azores, 2024

@misc{teodoro2024_1775514883072,
	author = "M. Filomena Teodoro and José V. Alves and Andrade, M. A. P.",
	title = "Modeling Metereological Data by Kalman Filter Approach",
	year = "2024",
	howpublished = "Ambos (impresso e digital)",
	url = "https://archive.icieng.eu/2024/index.html"
}

TY  - CPAPER
TI  - Modeling Metereological Data by Kalman Filter Approach
T2  - International Conference Innovation in Engineering
AU  - M. Filomena Teodoro
AU  - José V. Alves
AU  - Andrade, M. A. P.
PY  - 2024
CY  - São Miguel Island – Azores
UR  - https://archive.icieng.eu/2024/index.html
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  -