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

A BP neural network and target detection technology, applied in the field of turbulent target detection based on BP neural network multi-class classification, can solve the problems of high computational complexity, poor performance, and poor spectral estimation performance, and achieve effective turbulent target detection. Effect

Active Publication Date: 2018-10-26
SHANGHAI JIAO TONG UNIV
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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 rat

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

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Experimental program
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Embodiment Construction

[0020] This embodiment includes the following steps:

[0021] Step 1. Simulation data preparation:

[0022] The working wavelength λ of the airborne weather radar adopted in this embodiment is 0.03m, and the pulse repetition period T s is 0.001s, parameter k R About 1W·s 2 / m 2 . for different σ V Values, under different SNR conditions, according to the turbulent echo model to generate echo amplitude sequences with lengths of 8, 16, and 32, respectively, and the corresponding intensity levels form the training set and test set of the BP neural network to observe The influence of the values ​​of signal-to-noise ratio and sequence length on the final test results.

[0023] The turbulence echo model refers to: in view of the meteorological target radar echo signal in the turbulent area is a correlated random process, the correlation coefficient between two adjacent pulse echoes of the same meteorological target is: Among them: T s is the radar pulse repetition period; λ ...

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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...

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Application Information

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IPC IPC(8): G01S7/41
CPCG01S7/417
Inventor 肖刚张强赵俊豪王彦然刘艺博
Owner SHANGHAI JIAO TONG UNIV
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