Turbulence target detection method based on BP neural network multi-class classification

Abstract

The invention provides a turbulence target detection method based on BP neural network multi-class classification. The method comprises the step of according to a turbulence echo mode, under differentsignal to noise ratio conditions, generating echo amplitude sequences, and training sets and test sets of a BP neural network formed by corresponding strength grades so as to allow the trained BP neural network to divide the turbulence intensity into multiple grades, thereby achieving turbulence detection. According to the invention, an experience formulas and parameterization models are not required, by use of the multi-class classification function of the neural network, the echo amplitude sequences of a weather target radar are used as input data of the training set of the neural network;the turbulence intensity grades are used as output data of the training set; the relation between the radar echoes and turbulence intensity can be effectively determined only through learning of massecho data; and an objective that turbulence detection is achieved by use of the neural network to classify the turbulence intensities can be achieved.

Description

technical field [0001] The invention relates to a technology in the field of meteorological environment monitoring, in particular to a turbulent target detection method based on BP neural network multi-category classification. Background technique [0002] At present, most meteorological turbulence detection methods use empirical formulas and parametric models. The correctness of empirical formulas and parameter models greatly affects the accuracy of detection results. Among them, the detection of turbulent targets through definite physical principles is called non-parametric method or empirical formula method. Although the existing turbulence intensity detection methods have their own advantages, they also have obvious disadvantages, including poor performance in the case of low signal-to-noise ratio, poor spectral estimation performance under the condition of short echo data length, and methods The calculation complexity itself is high, and the speed of operation cannot b...

AI Technical Summary

Problems solved by technology

Although the existing turbulence intensity detection methods have their own advantages, they also have obvious disadvantages, including poor performance in the case of low signal-to-noise ratio, poor spectral estimation performance under the condition of short echo data length, and methods The calculation complexity itself is high, and the speed of operation cannot be guaranteed

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