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.

Supported by Portuguese funds through the Center of Naval Research, Naval Academy, Portugal and The Portuguese Foundation for Science and Technology, through the Center for Computational and Stochastic Mathematics, project UIDB/Multi/04621/2020.

Kalman filter,meteorological sensors,computational algorithm