PROCESSING OF DATA FROM FIELD OBSERVATIONS OF WATER FLOW VELOCITY PROFILES IN A SHALLOW RESERVOIR BASED ON THE KALMAN FILTER

Abstract

The paper is devoted to the numerical implementation of the Kalman filter algorithm for processing data from natural experiments obtained during expedition work to research the ecological state of the Azov Sea. Due to the use of various types of measuring devices placed on a research vessel, there is a problem of interference in the signal. To analyze the data of full-scale measurements of the profile of the three-dimensional velocity vector of the water environment’s movement in the Eastern part of the Sea of Azov at the qualitative and quantitative levels, sensor readings with a minimum amount of interference are necessary. In this paper, we selected the Kalman filter algorithm that allows us to obtain the minimum dispersion of the unbiased estimate of the dynamic system’s state. The Kalman filter is an algorithm that can be used to analyze measurements that include not only the desired measurements of the observed parameter or value, but also noises. Based on the full-scale velocity profiles measured by the ADCP probe at some points of the shallow water, a data filtering algorithm was constructed. A software module has been developed for solving the problem of filtering data of water flow velocities fields. The solution to the problem of constructing a filtration model is based on the two-stage Kalman algorithm. Estimates of the coefficients describing the error of the model and the measuring device are obtained. Natural measurements of the water flow velocity are used as input data for solving the filtration problem. Measurements were recorded at one second intervals every 10 centimeters. 128 measurements of each of the components of the velocity vector were performed in depth at the current time. The implementation of the Kalman filter algorithm for field measurements of the velocity field of the water flow of the Azov Sea was carried out using: the Python programming language, the PyCharm integrated development environment. PyCharm makes development as productive as possible with code completion and analysis features, instant errors highlighting, and quick fixes. With PyCharm, smart code updates are available with safe deletion and renaming, a extract method, an input variable, an inline variable or method, and other refactorings. Framework and programming language oriented refactorings will help you make any change within an entire project. Processing of natural measurements based on Kalman’s filtering makes it possible not only to refine the input data for the mathematical model problem of hydrodynamics, but also to significantly reduce the error of its solution.