CN115542279A - Weather radar clutter classification and identification method and device - Google Patents

Weather radar clutter classification and identification method and device Download PDF

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CN115542279A
CN115542279A CN202211156539.7A CN202211156539A CN115542279A CN 115542279 A CN115542279 A CN 115542279A CN 202211156539 A CN202211156539 A CN 202211156539A CN 115542279 A CN115542279 A CN 115542279A
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徐红祥
江结林
张佩
许小龙
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Nanjing University of Information Science and Technology
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/411Identification of targets based on measurements of radar reflectivity
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/95Radar or analogous systems specially adapted for specific applications for meteorological use
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/95Radar or analogous systems specially adapted for specific applications for meteorological use
    • G01S13/958Theoretical aspects
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414Discriminating targets with respect to background clutter
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/417Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks
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Abstract

The invention discloses a method and a device for classifying and identifying meteorological radar clutter, wherein the method comprises the steps of obtaining tested meteorological radar data and preprocessing the meteorological radar data; inputting the preprocessed tested meteorological radar data into the trained SegNet network model to obtain a classification recognition result; the training process of the SegNet network model comprises the following steps: acquiring and preprocessing trained meteorological radar data; integrating the preprocessed meteorological radar data into a corresponding training data set based on the data type; respectively calculating the standard deviation of a training data set corresponding to the radar reflectivity and the differential phase as texture data of the radar reflectivity and the differential phase; processing training data and texture data in the training data set by adopting a fuzzy logic algorithm to obtain a particle phase state type label set; initializing a SegNet network model, and performing iterative training based on a training data set and a label set; the method can effectively classify and identify the meteorological radar clutter so as to obtain high-quality meteorological radar data.

Description

一种气象雷达杂波分类识别方法及装置Method and device for classifying and identifying meteorological radar clutter

技术领域technical field

本发明涉及一种气象雷达杂波分类识别方法及装置,属于气象雷达技术领域。The invention relates to a meteorological radar clutter classification and recognition method and device, belonging to the technical field of meteorological radar.

背景技术Background technique

对气象雷达数据进行分析处理的重要前提是建立智能的气象雷达质量控制模型。雷达杂波的识别与分类在多个领域有着重要的应用价值。在航空领域,不仅对复杂天气带来的航空危险有着预警作用,还能为航线规划提供决策依据;在人工影响天气领域,不仅可以提高对降水定量检测精度,而且能为人工影响天气的运行决策和评估提供重要的参考依据。An important prerequisite for analyzing and processing weather radar data is to establish an intelligent weather radar quality control model. The identification and classification of radar clutter has important application value in many fields. In the field of aviation, it not only has an early warning effect on the aviation hazards brought about by complex weather, but also provides a decision-making basis for route planning; and evaluation provide an important reference basis.

为了获得质量较高的气象雷达数据,一个首要的问题就是合理地区分出气象回波与非气象回波。雷达杂波的分类识别方法包括:统计决策方法、判决图方法等。统计决策方法综合了降水粒子及杂波粒子的偏振特性及前人的研究经验,通过设定不同降水粒子与杂波粒子的偏振参量门限值实现降水粒子与杂波粒子的分类,但该方法的门限值一般固定,因此当研究的目标区域内环境发生变化时,分类精度将受到很大影响。判决图方法根据预先确定的类型边界来对降水粒子与杂波粒子进行分类,但由于气象雷达接收到的不同降水粒子与杂波粒子的协方差矩阵不是相互独立的,会使得判决图方法的分类精度受到一定的影响。对于降水粒子与杂波粒子的分类识别任务,需要解决传统算法与人工检测的智能化低的问题。In order to obtain high-quality weather radar data, one of the most important problems is to reasonably distinguish between meteorological echoes and non-meteorological echoes. The classification and recognition methods of radar clutter include: statistical decision-making methods, decision-making graph methods, etc. The statistical decision-making method integrates the polarization characteristics of precipitation particles and clutter particles and previous research experience, and realizes the classification of precipitation particles and clutter particles by setting the polarization parameter threshold values of different precipitation particles and clutter particles. The threshold value of is generally fixed, so when the environment in the research target area changes, the classification accuracy will be greatly affected. The decision diagram method classifies precipitation particles and clutter particles according to the predetermined type boundary, but because the covariance matrix of different precipitation particles and clutter particles received by the weather radar is not independent of each other, the classification of the decision diagram method will be difficult. Accuracy is somewhat affected. For the classification and recognition tasks of precipitation particles and clutter particles, it is necessary to solve the problem of low intelligence of traditional algorithms and manual detection.

发明内容Contents of the invention

本发明的目的在于克服现有技术中的不足,提供一种气象雷达杂波分类识别方法及装置,能够有效的对气象雷达杂波进行分类识别,从而获得质量较高的气象雷达数据。The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and device for classifying and identifying weather radar clutter, which can effectively classify and identify weather radar clutter, thereby obtaining weather radar data with high quality.

为达到上述目的,本发明是采用下述技术方案实现的:In order to achieve the above object, the present invention is achieved by adopting the following technical solutions:

第一方面,本发明提供了一种气象雷达杂波分类识别方法,包括:In a first aspect, the present invention provides a method for classifying and identifying weather radar clutter, comprising:

获取测试的气象雷达数据并进行预处理;Obtain and preprocess the tested weather radar data;

将预处理后的测试的气象雷达数据输入训练好的SegNet网络模型中,获取分类识别结果;Input the preprocessed test weather radar data into the trained SegNet network model to obtain classification recognition results;

其中,所述SegNet网络模型的训练过程包括:Wherein, the training process of described SegNet network model comprises:

获取训练的气象雷达数据并进行预处理;所述气象雷达数据包括雷达反射率、差分反射率、差分传播相移率、相关系数及微分相位;Obtain the weather radar data of training and carry out preprocessing; The weather radar data includes radar reflectivity, differential reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase;

基于数据类型将预处理后的气象雷达数据整合成对应的训练数据集;Based on the data type, the preprocessed weather radar data is integrated into a corresponding training data set;

分别计算雷达反射率和微分相位对应的训练数据集的标准差作为雷达反射率和微分相位的纹理数据;Calculate the standard deviation of the training data set corresponding to the radar reflectivity and differential phase respectively as the texture data of radar reflectivity and differential phase;

采用模糊逻辑算法处理训练数据集中训练数据与纹理数据获取粒子相态类型的标签集;The fuzzy logic algorithm is used to process the training data and texture data in the training data set to obtain the label set of the particle phase type;

初始化SegNet网络模型,并基于训练数据集和标签集进行迭代训练,直至达到预设的最大迭代次数或SegNet网络模型的权重参数收敛。Initialize the SegNet network model, and perform iterative training based on the training data set and label set until the preset maximum number of iterations or the weight parameters of the SegNet network model converge.

可选的,所述预处理包括数据清洗和尺度扩充;Optionally, the preprocessing includes data cleaning and scale expansion;

所述数据清洗包括查找气象雷达数据中的NaN数据值以及小于零的雷达反射率,并分别置零;Described data cleaning comprises finding NaN data value and the radar reflectivity less than zero in weather radar data, and set zero respectively;

所述尺度扩充包括将气象雷达数据采取左上对齐、右下补零的方式进行扩充。The scale expansion includes expanding the meteorological radar data by aligning the upper left and filling the lower right with zeros.

可选的,所述分别计算雷达反射率和微分相位对应的训练数据集的标准差包括:Optionally, said calculating the standard deviation of the training data set corresponding to the radar reflectivity and the differential phase respectively includes:

遍历雷达反射率对应的训练数据集,计算1km范围内雷达反射率的标准差SD(ZH):Traverse the training data set corresponding to the radar reflectivity, and calculate the standard deviation SD (Z H ) of the radar reflectivity within 1km:

Figure BDA0003858997630000021
Figure BDA0003858997630000021

Figure BDA0003858997630000031
Figure BDA0003858997630000031

式中,m(ZH)为1km范围内雷达反射率ZH的平均值,nZ为1km范围内雷达反射率的数据点个数;In the formula, m(Z H ) is the average value of radar reflectivity Z H within 1 km, and n Z is the number of data points of radar reflectivity within 1 km;

遍历微分相位对应的训练数据集,计算2km范围内微分相位的标准差Traverse the training data set corresponding to the differential phase, and calculate the standard deviation of the differential phase within 2km

Figure BDA0003858997630000032
Figure BDA0003858997630000032

Figure BDA0003858997630000033
Figure BDA0003858997630000033

式中,m(φDP)为2km范围内微分相位φDP的平均值,nφ为2km范围内微分相位的数据点个数。In the formula, m(φ DP ) is the average value of differential phase φ DP within 2km, and n φ is the number of data points of differential phase within 2km.

可选的,所述采用模糊逻辑算法处理训练数据集与纹理数据获取粒子相态类型的标签集包括:Optionally, the step of processing the training data set and the texture data using the fuzzy logic algorithm to obtain the tag set of the particle phase state type includes:

采用梯形函数作为模糊逻辑算法的隶属度函数,将训练数据集中的训练数据与纹理数据作为输入参数进行模糊逻辑运算,获取粒子相态类型对应的隶属度作为模糊逻辑输出;梯形函数为:The trapezoidal function is used as the membership function of the fuzzy logic algorithm, and the training data and texture data in the training data set are used as input parameters to perform fuzzy logic operations, and the membership degree corresponding to the particle phase state type is obtained as the fuzzy logic output; the trapezoidal function is:

Figure BDA0003858997630000034
Figure BDA0003858997630000034

式中,X1、X2、X3、X4为梯形函数的阈值参数,P(j)(xi)为第j个输入参数对第i种粒子相态类型的隶属度;In the formula, X 1 , X 2 , X 3 , and X 4 are the threshold parameters of the trapezoidal function, and P (j) ( xi ) is the membership degree of the jth input parameter to the i-th particle phase state type;

采用加权平均判决法对模糊逻辑输出进行退模糊化,获取粒子相态类型对应的隶属度集成值:The weighted average judgment method is used to defuzzify the fuzzy logic output, and the membership degree integration value corresponding to the particle phase state type is obtained:

Figure BDA0003858997630000041
Figure BDA0003858997630000041

式中,Si为第j个输入参数对第i种粒子相态类型的隶属度集成值,Wij为第i种粒子相态类型第j个输入参数的权重系数,J为输入参数数量;In the formula, S i is the integrated membership value of the jth input parameter to the i-th particle phase state type, W ij is the weight coefficient of the j-th input parameter of the i-th particle phase state type, and J is the number of input parameters;

若输入参数的隶属度集成值为0,则输入参数对应的粒子相态类型为无气象回波,将其标签设置为0;If the integrated value of the membership degree of the input parameter is 0, then the particle phase state type corresponding to the input parameter is no meteorological echo, and its label is set to 0;

若输入参数的隶属度集成值大于设定阈值,则输入参数对应的粒子相态类型为杂波,将其标签设置为1;If the integrated value of the membership degree of the input parameter is greater than the set threshold, the particle phase state type corresponding to the input parameter is clutter, and its label is set to 1;

若输入参数的隶属度集成值小于设定阈值且大于0,则输入参数对应的粒子相态类型为其他,将其标签设置为2;If the integrated value of the membership degree of the input parameter is less than the set threshold and greater than 0, then the particle phase state type corresponding to the input parameter is other, and its label is set to 2;

根据输入参数的标签构建粒子相态类型的标签集。Builds a label set for the particle phase type based on the labels of the input parameters.

可选的,所述基于训练数据集和标签集进行迭代训练包括:Optionally, the iterative training based on the training data set and the label set includes:

基于SegNet网络模型的Encoder网络下采样训练数据,获得数据特征;The Encoder network based on the SegNet network model downsamples the training data to obtain data features;

基于SegNet网络模型的Decoder网络上采样特征数据,获取预测结果;Sampling characteristic data on Decoder network based on SegNet network model to obtain prediction results;

基于交叉熵损失函数计算预测结果与其标签的损失,并根据损失反向传播更新SegNet网络模型的权重参数。Calculate the loss of the prediction result and its label based on the cross-entropy loss function, and update the weight parameters of the SegNet network model according to the loss backpropagation.

可选的,所述Encoder网络包括:Optionally, the Encoder network includes:

第一层,依次由两个64×3×3的卷积以及最大池化下采样组成;The first layer consists of two 64×3×3 convolutions and maximum pooling downsampling in turn;

第二层,依次由两个128×3×3的卷积以及最大池化下采样组成;The second layer consists of two 128×3×3 convolutions and maximum pooling downsampling in turn;

第三层,依次由三个256×3×3的卷积以及最大池化下采样组成;The third layer consists of three 256×3×3 convolutions and maximum pooling downsampling in turn;

第四层,依次由三个512×3×3的卷积以及最大池化下采样组成;The fourth layer consists of three 512×3×3 convolutions and maximum pooling downsampling in turn;

第五层,依次由三个5212×3×3的卷积以及最大池化下采样组成;The fifth layer consists of three 5212×3×3 convolutions and maximum pooling downsampling in turn;

所述Decoder网络包括:The Decoder network includes:

第一层,依次由反最大池化上采样以及三个521×3×3的卷积组成;The first layer consists of inverse maximum pooling upsampling and three 521×3×3 convolutions;

第二层,依次由反最大池化上采样以及三个521×3×3的卷积组成;The second layer consists of inverse maximum pooling upsampling and three 521×3×3 convolutions;

第三层,依次由反最大池化上采样以及两个256×3×3的卷积组成;The third layer consists of inverse maximum pooling upsampling and two 256×3×3 convolutions;

第四层,依次由反最大池化上采样以及两个128×3×3的卷积组成;The fourth layer consists of anti-maximum pooling upsampling and two 128×3×3 convolutions;

第五层,依次由反最大池化上采样以及两个64×3×3的卷积组成。The fifth layer, in turn, consists of anti-maximum pooling upsampling and two 64×3×3 convolutions.

可选的,所述迭代训练的过程中,通过Pixel Accuracy作为精度评价指标,获取预测结果和标签的接近程度。Optionally, during the iterative training process, Pixel Accuracy is used as an accuracy evaluation index to obtain the closeness between the prediction result and the label.

第二方面,本发明提供了一种气象雷达杂波分类识别装置,所述装置包括:In a second aspect, the present invention provides a device for classifying and identifying weather radar clutter, said device comprising:

预处理模块,用于获取测试的气象雷达数据并进行预处理;The preprocessing module is used to obtain and preprocess the weather radar data of the test;

分类识别模块,用于将预处理后的测试的气象雷达数据输入训练好的SegNet网络模型中,获取分类识别结果;The classification recognition module is used to input the weather radar data of the preprocessed test into the trained SegNet network model to obtain the classification recognition result;

模型训练模块,用于:Model training module for:

获取训练的气象雷达数据并进行预处理;所述气象雷达数据包括雷达反射率、差分反射率、差分传播相移率、相关系数及微分相位;Obtain the weather radar data of training and carry out preprocessing; The weather radar data includes radar reflectivity, differential reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase;

基于数据类型将预处理后的气象雷达数据整合成对应的训练数据集;Based on the data type, the preprocessed weather radar data is integrated into a corresponding training data set;

分别计算雷达反射率和微分相位对应的训练数据集的标准差作为雷达反射率和微分相位的纹理数据;Calculate the standard deviation of the training data set corresponding to the radar reflectivity and differential phase respectively as the texture data of radar reflectivity and differential phase;

采用模糊逻辑算法处理训练数据集中训练数据与纹理数据获取粒子相态类型的标签集;The fuzzy logic algorithm is used to process the training data and texture data in the training data set to obtain the label set of the particle phase type;

初始化SegNet网络模型,并基于训练数据集和标签集进行迭代训练,直至达到预设的最大迭代次数或SegNet网络模型的权重参数收敛。Initialize the SegNet network model, and perform iterative training based on the training data set and label set until the preset maximum number of iterations or the weight parameters of the SegNet network model converge.

第三方面,本发明提供了一种气象雷达杂波分类识别装置,包括处理器及存储介质;In a third aspect, the present invention provides a weather radar clutter classification and identification device, including a processor and a storage medium;

所述存储介质用于存储指令;The storage medium is used to store instructions;

所述处理器用于根据所述指令进行操作以执行根据上述方法的步骤。The processor is configured to operate in accordance with the instructions to perform the steps according to the method described above.

第四方面,本发明提供了计算机可读存储介质,其上存储有计算机程序,该程序被处理器执行时实现上述方法的步骤。In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

与现有技术相比,本发明所达到的有益效果:Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

本发明提供的一种气象雷达杂波分类识别方法及装置,通过获取雷达反射率、差分反射率、差分传播相移率、相关系数及微分相位构建五通道的训练数据集,通过模糊逻辑算法获取训练数据的标签集,通过训练数据集和标签集对SegNet网络模型进行训练,通过训练好的SegNet网络模型进行气象雷达杂波分类识别,能够有效的对气象雷达杂波进行分类识别,从而获得质量较高的气象雷达数据。A method and device for classifying and identifying meteorological radar clutter provided by the present invention constructs a five-channel training data set by obtaining radar reflectivity, differential reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase, and obtains it through a fuzzy logic algorithm. The label set of the training data, the SegNet network model is trained through the training data set and the label set, and the weather radar clutter is classified and identified through the trained SegNet network model, which can effectively classify and identify the weather radar clutter, thereby obtaining quality Higher weather radar data.

附图说明Description of drawings

图1是本发明实施例一提供的一种气象雷达杂波分类识别方法的流程图。FIG. 1 is a flow chart of a method for classifying and identifying weather radar clutter provided by Embodiment 1 of the present invention.

具体实施方式detailed description

下面结合附图对本发明作进一步描述。以下实施例仅用于更加清楚地说明本发明的技术方案,而不能以此来限制本发明的保护范围。The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

实施例一:Embodiment one:

如图1所示,本发明提供了一种气象雷达杂波分类识别方法,包括:As shown in Figure 1, the present invention provides a kind of meteorological radar clutter classification identification method, comprising:

1、获取测试的气象雷达数据并进行预处理;1. Obtain and preprocess the tested weather radar data;

2、将预处理后的测试的气象雷达数据输入训练好的SegNet网络模型中,获取分类识别结果;2. Input the preprocessed test weather radar data into the trained SegNet network model to obtain classification and recognition results;

其中,SegNet网络模型的训练过程包括:Among them, the training process of the SegNet network model includes:

S101、获取训练的气象雷达数据并进行预处理;S101. Acquiring weather radar data for training and performing preprocessing;

S102、基于数据类型将预处理后的气象雷达数据整合成对应的训练数据集;S102. Based on the data type, integrate the preprocessed weather radar data into a corresponding training data set;

S103、分别计算雷达反射率和微分相位对应的训练数据集的标准差作为雷达反射率和微分相位的纹理数据;S103, respectively calculating the standard deviation of the training data set corresponding to the radar reflectivity and the differential phase as the texture data of the radar reflectivity and the differential phase;

S104、采用模糊逻辑算法处理训练数据集中训练数据与纹理数据获取粒子相态类型的标签集;S104, using a fuzzy logic algorithm to process the training data and texture data in the training data set to obtain a label set of the particle phase type;

S105、初始化SegNet网络模型,并基于训练数据集和标签集进行迭代训练,直至达到预设的最大迭代次数或SegNet网络模型的权重参数收敛。S105. Initialize the SegNet network model, and perform iterative training based on the training data set and the label set, until reaching the preset maximum number of iterations or convergence of weight parameters of the SegNet network model.

其中,基于训练数据集和标签集进行迭代训练包括:Among them, iterative training based on the training data set and label set includes:

S201、基于SegNet网络模型的Encoder网络下采样训练数据,获得数据特征;S201. The Encoder network based on the SegNet network model downsamples the training data to obtain data features;

S202、基于SegNet网络模型的Decoder网络上采样特征数据,获取预测结果;S202. Sampling feature data on the Decoder network based on the SegNet network model to obtain prediction results;

S203、基于交叉熵损失函数计算预测结果与其标签的损失,并根据损失反向传播更新SegNet网络模型的权重参数。S203. Calculate the loss of the predicted result and its label based on the cross-entropy loss function, and update the weight parameters of the SegNet network model according to the loss backpropagation.

(1)本实施例提供的气象雷达数据包括雷达反射率、差分反射率、差分传播相移率、相关系数及微分相位,并通过numpy文件进行保存。(1) The meteorological radar data provided in this embodiment include radar reflectivity, differential reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase, and are saved through numpy files.

(2)预处理包括数据清洗和尺度扩充;(2) Preprocessing includes data cleaning and scale expansion;

数据清洗包括查找气象雷达数据中的NaN(空数据)数据值以及小于零的雷达反射率,并分别置零;Data cleaning includes finding NaN (null data) data values and radar reflectivity less than zero in weather radar data, and setting them to zero respectively;

尺度扩充包括将气象雷达数据采取左上对齐、右下补零的方式进行扩充,一般采集到的数据尺寸为336×920,为了满足SegNet网络模型的最大池化操作需要扩充为384×1088。Scale expansion includes expanding the meteorological radar data by aligning the upper left and filling the lower right with zeros. Generally, the size of the collected data is 336×920. In order to meet the maximum pooling operation of the SegNet network model, it needs to be expanded to 384×1088.

(3)分别计算雷达反射率和微分相位对应的训练数据集的标准差包括:(3) Calculate the standard deviation of the training data set corresponding to the radar reflectivity and differential phase respectively:

遍历雷达反射率对应的训练数据集,计算1km范围内雷达反射率的标准差SD(ZH):Traverse the training data set corresponding to the radar reflectivity, and calculate the standard deviation SD (Z H ) of the radar reflectivity within 1km:

Figure BDA0003858997630000071
Figure BDA0003858997630000071

Figure BDA0003858997630000072
Figure BDA0003858997630000072

式中,m(ZH)为1km范围内雷达反射率ZH的平均值,nZ为1km范围内雷达反射率的数据点个数;In the formula, m(Z H ) is the average value of radar reflectivity Z H within 1 km, and n Z is the number of data points of radar reflectivity within 1 km;

遍历微分相位对应的训练数据集,计算2km范围内微分相位的标准差Traverse the training data set corresponding to the differential phase, and calculate the standard deviation of the differential phase within 2km

Figure BDA0003858997630000073
Figure BDA0003858997630000073

Figure BDA0003858997630000081
Figure BDA0003858997630000081

式中,m(φDP)为2km范围内微分相位φDP的平均值,nφ为2km范围内微分相位的数据点个数。In the formula, m(φ DP ) is the average value of differential phase φ DP within 2km, and n φ is the number of data points of differential phase within 2km.

(4)采用模糊逻辑算法处理训练数据集与纹理数据获取粒子相态类型的标签集包括:(4) Use the fuzzy logic algorithm to process the training data set and texture data to obtain the label set of the particle phase type, including:

采用梯形函数作为模糊逻辑算法的隶属度函数,将训练数据集中的训练数据与纹理数据作为输入参数进行模糊逻辑运算,获取粒子相态类型对应的隶属度作为模糊逻辑输出;梯形函数为:The trapezoidal function is used as the membership function of the fuzzy logic algorithm, and the training data and texture data in the training data set are used as input parameters to perform fuzzy logic operations, and the membership degree corresponding to the particle phase state type is obtained as the fuzzy logic output; the trapezoidal function is:

Figure BDA0003858997630000082
Figure BDA0003858997630000082

式中,X1、X2、X3、X4为梯形函数的阈值参数,P(j)(xi)为第j个输入参数对第i种粒子相态类型的隶属度;In the formula, X 1 , X 2 , X 3 , and X 4 are the threshold parameters of the trapezoidal function, and P (j) ( xi ) is the membership degree of the jth input parameter to the i-th particle phase state type;

采用加权平均判决法对模糊逻辑输出进行退模糊化,获取粒子相态类型对应的隶属度集成值:The weighted average judgment method is used to defuzzify the fuzzy logic output, and the membership degree integration value corresponding to the particle phase state type is obtained:

Figure BDA0003858997630000083
Figure BDA0003858997630000083

式中,Si为第j个输入参数对第i种粒子相态类型的隶属度集成值,Wij为第i种粒子相态类型第j个输入参数的权重系数,J为输入参数数量;In the formula, S i is the integrated membership value of the jth input parameter to the i-th particle phase state type, W ij is the weight coefficient of the j-th input parameter of the i-th particle phase state type, and J is the number of input parameters;

若输入参数的隶属度集成值为0,则输入参数对应的粒子相态类型为无气象回波,将其标签设置为0;If the integrated value of the membership degree of the input parameter is 0, then the particle phase state type corresponding to the input parameter is no meteorological echo, and its label is set to 0;

若输入参数的隶属度集成值大于设定阈值,则输入参数对应的粒子相态类型为杂波,将其标签设置为1;If the integrated value of the membership degree of the input parameter is greater than the set threshold, the particle phase state type corresponding to the input parameter is clutter, and its label is set to 1;

若输入参数的隶属度集成值小于设定阈值且大于0,则输入参数对应的粒子相态类型为其他,将其标签设置为2;If the integrated value of the membership degree of the input parameter is less than the set threshold and greater than 0, then the particle phase state type corresponding to the input parameter is other, and its label is set to 2;

根据输入参数的标签构建粒子相态类型的标签集。Builds a label set for the particle phase type based on the labels of the input parameters.

(5)将numpy类型的训练数据集转换为(5×384×1088)的tensor类型数据,组合训练数据与其对应标签成一个dataset。设置训练数据集的batch为1,利用pytorch中的DataLoader方法,加载尺寸为(1×channel×384×1088)的tensor,其中训练数据的channel为5(5种数据类型的训练数据作为5个输入通道数据),标签的channel为1。(5) Convert the numpy-type training data set into (5×384×1088) tensor-type data, and combine the training data and its corresponding labels into a dataset. Set the batch of the training data set to 1, and use the DataLoader method in pytorch to load a tensor with a size of (1×channel×384×1088), where the channel of the training data is 5 (5 types of training data are used as 5 inputs channel data), the channel of the label is 1.

设计Encoder网络的结构为:Design the structure of the Encoder network as follows:

第一层,依次由两个64×3×3的卷积以及最大池化下采样组成;The first layer consists of two 64×3×3 convolutions and maximum pooling downsampling in turn;

第二层,依次由两个128×3×3的卷积以及最大池化下采样组成;The second layer consists of two 128×3×3 convolutions and maximum pooling downsampling in turn;

第三层,依次由三个256×3×3的卷积以及最大池化下采样组成;The third layer consists of three 256×3×3 convolutions and maximum pooling downsampling in turn;

第四层,依次由三个512×3×3的卷积以及最大池化下采样组成;The fourth layer consists of three 512×3×3 convolutions and maximum pooling downsampling in turn;

第五层,依次由三个5212×3×3的卷积以及最大池化下采样组成;The fifth layer consists of three 5212×3×3 convolutions and maximum pooling downsampling in turn;

依照该网络结构,处理(1×5×384×1088)的训练数据,得到(1×512×6×17)的特征数据,其中512代表特征通道数。According to the network structure, process (1×5×384×1088) training data to obtain (1×512×6×17) feature data, where 512 represents the number of feature channels.

设计Decoder网络结构为:Design the Decoder network structure as:

第一层,依次由反最大池化上采样以及三个521×3×3的卷积组成;The first layer consists of inverse maximum pooling upsampling and three 521×3×3 convolutions;

第二层,依次由反最大池化上采样以及三个521×3×3的卷积组成;The second layer consists of inverse maximum pooling upsampling and three 521×3×3 convolutions;

第三层,依次由反最大池化上采样以及两个256×3×3的卷积组成;The third layer consists of inverse maximum pooling upsampling and two 256×3×3 convolutions;

第四层,依次由反最大池化上采样以及两个128×3×3的卷积组成;The fourth layer consists of anti-maximum pooling upsampling and two 128×3×3 convolutions;

第五层,依次由反最大池化上采样以及两个64×3×3的卷积组成。The fifth layer, in turn, consists of anti-maximum pooling upsampling and two 64×3×3 convolutions.

依照该网络结构,处理(1×512×6×17)的特征数据,得到(1×3×384×1088)的预测数据,其中3代表通道数对应标签的三种类别。According to the network structure, the feature data of (1×512×6×17) is processed to obtain the prediction data of (1×3×384×1088), where 3 represents the three categories of labels corresponding to the number of channels.

其中,Encoder网络还包括:Among them, the Encoder network also includes:

设置SegNet网络模型中Encoder网络的卷积核的参数size为3,stride为1,padding参数为1,表示采用3×3移动步幅为1的正方形卷积核,当卷积操作超越数据边缘时,在数据周围补一圈零以防止越界异常。不同卷积层都采用该same卷积,提取训练数据特征;卷积操作的表达式如下:Set the parameter size of the convolution kernel of the Encoder network in the SegNet network model to 3, the stride to 1, and the padding parameter to 1, indicating that a square convolution kernel with a 3×3 moving step of 1 is used. When the convolution operation exceeds the edge of the data , pad a circle of zeros around the data to prevent out-of-bounds exceptions. Different convolutional layers use the same convolution to extract training data features; the expression of the convolution operation is as follows:

Figure BDA0003858997630000101
Figure BDA0003858997630000101

Figure BDA0003858997630000102
Figure BDA0003858997630000102

式中,Hin表示输入数据的长度,Win表示输入数据的宽度,KH代表卷积核的长度,KW代表卷积核的宽度,P代表补零的圈数,S代表卷积核的移动步幅。In the formula, H in represents the length of the input data, Win represents the width of the input data, K H represents the length of the convolution kernel, K W represents the width of the convolution kernel, P represents the number of zero padding circles, and S represents the convolution kernel movement stride.

由于每次卷积后的数据分布特征存在不一致的情况,使得后一卷积层需不断去适应前一卷积层的输出变化,降低了网络中层与层之间的解耦。利用批标准化对卷积前的数据进行归一化,固定其分布特征,使得网络学习更加稳定。批标准化的表达式如下:Due to the inconsistency of the data distribution characteristics after each convolution, the latter convolutional layer needs to continuously adapt to the output changes of the previous convolutional layer, which reduces the decoupling between layers in the network. Batch normalization is used to normalize the data before convolution and fix its distribution characteristics, making network learning more stable. The expression for batch normalization is as follows:

Figure BDA0003858997630000103
Figure BDA0003858997630000103

式中,(xi)(b)表示输入当前批的bth样本时该层ith输入节点的值,μ(xi)为输入数据的均值,σ(xi)为输入数据的标准差,∈为防止除零引入的极小数值,γ和β为待学习的scale和shift参数。In the formula, ( xi ) (b) represents the value of the i th input node of the layer when the b th sample of the current batch is input, μ( xi ) is the mean value of the input data, and σ( xi ) is the standard deviation of the input data , ∈ is to prevent the extremely small value introduced by zero division, γ and β are the scale and shift parameters to be learned.

使用Relu激活函数,增加各卷积层之间的非线性关系,避免出现梯度消失的情况。Relu会使一部分卷积输出为0,这样就造成了网络的稀疏性,并且减少了参数的相互依存关系,缓解了过拟合问题的发生。Relu的表达式如下:Use the Relu activation function to increase the nonlinear relationship between the convolutional layers to avoid the disappearance of the gradient. Relu will make a part of the convolution output 0, which causes the sparsity of the network, reduces the interdependence of parameters, and alleviates the occurrence of overfitting problems. The expression of Relu is as follows:

f(x)=max(0,x)f(x)=max(0,x)

对Relu后的数据,进行最大池化下采样,摘取这些带有更多信息的数据点,有效地将冗余信息摘除,实现特征的提炼。下采样的同时,保存最大值的索引信息,最大池化的表达式如下:For the data after Relu, perform maximum pooling down-sampling, extract these data points with more information, effectively remove redundant information, and realize feature extraction. While downsampling, the index information of the maximum value is saved. The expression of the maximum pooling is as follows:

Figure BDA0003858997630000104
Figure BDA0003858997630000104

式中,f表示池化核的尺寸大小,s表示池化核移动的步长。In the formula, f represents the size of the pooling kernel, and s represents the step size of the pooling kernel movement.

Decoder网络还包括:The Decoder network also includes:

反池化卷积操作后的特征数据,逐步恢复数据的尺寸大小。由于最大池化会记录最大值的索引,所以反池化时直接将索引位置的数据还原,其它位置填0,从而逐步恢复数据的原尺寸大小。反池化的表达式如下:Unpool the feature data after the convolution operation, and gradually restore the size of the data. Since the maximum pooling will record the index with the maximum value, the data at the index position is directly restored during unpooling, and the other positions are filled with 0, thereby gradually restoring the original size of the data. The expression of anti-pooling is as follows:

Xout=(Xin-1)×s+fX out =(X in -1)×s+f

Decoder网络采用same卷积减少反池化后的数据特征通道,卷积核的参数size为3,stride为1,padding参数为1,不同卷积层卷积前后数据的长宽尺寸不发生,仅改变特征的通道数量。对于卷积后的数据,采用批标准化稳定数据的分布特征,提升网络的学习能力。其次使用Relu激活函数,增加卷积层之间非线性,避免出现梯度消失的情况。The Decoder network uses the same convolution to reduce the data feature channels after unpooling. The parameter size of the convolution kernel is 3, the stride is 1, and the padding parameter is 1. The length and width of the data before and after convolution of different convolution layers do not occur, only Change the number of channels of the feature. For the convolutional data, batch normalization is used to stabilize the distribution characteristics of the data and improve the learning ability of the network. Secondly, the Relu activation function is used to increase the nonlinearity between the convolutional layers to avoid the disappearance of the gradient.

(6)基于交叉熵损失函数计算预测结果与其标签的损失包括:(6) Calculating the loss of the prediction result and its label based on the cross-entropy loss function includes:

使用softmax函数计算预测数据的各类别概率,每个通道上对应位置概率之和为1,其中概率最大值的所属通道索引即为该数据点的预测标签。Softmax的表达式如下:Use the softmax function to calculate the probability of each category of the predicted data. The sum of the corresponding position probabilities on each channel is 1, and the channel index of the maximum probability is the predicted label of the data point. The expression of Softmax is as follows:

Figure BDA0003858997630000111
Figure BDA0003858997630000111

式中,Xi为ith通道上的数据点。In the formula, Xi is the data point on the i th channel.

计算预测数据与标签之间的交叉熵损失作为预测值与真实值之间的损失,利用pytorch中的反向传播机制,更新SegNet网络模型中权值的梯度及参数值,提高SegNet的预测精度。交叉熵损失函数的表达式如下:Calculate the cross-entropy loss between the predicted data and the label as the loss between the predicted value and the real value, and use the backpropagation mechanism in pytorch to update the gradient and parameter values of the weights in the SegNet network model to improve the prediction accuracy of SegNet. The expression of the cross-entropy loss function is as follows:

Figure BDA0003858997630000112
Figure BDA0003858997630000112

式中,pi为真实标签值,qi为经过softmax计算后的预测数据值。In the formula, p i is the real label value, and q i is the predicted data value after softmax calculation.

(7)迭代训练的过程中,通过Pixel Accuracy作为精度评价指标,获取预测结果和标签的接近程度;(7) In the process of iterative training, use Pixel Accuracy as an accuracy evaluation index to obtain the closeness of the prediction result and the label;

根据超参数学习率的数值,选取适当的训练次数,不断训练SegNet网络模型。在训练迭代的过程中,保存当前损失最小的模型参数。网络训练完成后,载入最优网络模型,输入测试数据集,得到预测数据,使用Pixel Accuracy作为精度评价指标,查看预测数据与标签真实值的接近程度。Pixel Accuracy计算的表达式如下:According to the value of the hyperparameter learning rate, select the appropriate number of training times to continuously train the SegNet network model. During the training iterations, save the current model parameters with the smallest loss. After the network training is completed, load the optimal network model, input the test data set, and obtain the predicted data. Use Pixel Accuracy as the accuracy evaluation index to check the closeness of the predicted data to the true value of the label. The expression for Pixel Accuracy calculation is as follows:

Figure BDA0003858997630000121
Figure BDA0003858997630000121

式中,k为目标分类数,Pii为预测对的像素点数,Pij为属于i类却归为j类的像素点数。In the formula, k is the number of target categories, P ii is the number of pixels of the prediction pair, and P ij is the number of pixels belonging to class i but classified into class j.

实施例二:Embodiment two:

本发明实施例提供了一种气象雷达杂波分类识别装置,装置包括:An embodiment of the present invention provides a weather radar clutter classification and recognition device, the device comprising:

预处理模块,用于获取测试的气象雷达数据并进行预处理;The preprocessing module is used to obtain and preprocess the weather radar data of the test;

分类识别模块,用于将预处理后的测试的气象雷达数据输入训练好的SegNet网络模型中,获取分类识别结果;The classification recognition module is used to input the weather radar data of the preprocessed test into the trained SegNet network model to obtain the classification recognition result;

模型训练模块,用于:Model training module for:

获取训练的气象雷达数据并进行预处理;气象雷达数据包括雷达反射率、差分反射率、差分传播相移率、相关系数及微分相位;Obtain and preprocess the weather radar data for training; the weather radar data includes radar reflectivity, differential reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase;

基于数据类型将预处理后的气象雷达数据整合成对应的训练数据集;Based on the data type, the preprocessed weather radar data is integrated into a corresponding training data set;

分别计算雷达反射率和微分相位对应的训练数据集的标准差作为雷达反射率和微分相位的纹理数据;Calculate the standard deviation of the training data set corresponding to the radar reflectivity and differential phase respectively as the texture data of radar reflectivity and differential phase;

采用模糊逻辑算法处理训练数据集中训练数据与纹理数据获取粒子相态类型的标签集;The fuzzy logic algorithm is used to process the training data and texture data in the training data set to obtain the label set of the particle phase type;

初始化SegNet网络模型,并基于训练数据集和标签集进行迭代训练,直至达到预设的最大迭代次数或SegNet网络模型的权重参数收敛。Initialize the SegNet network model, and perform iterative training based on the training data set and label set until the preset maximum number of iterations or the weight parameters of the SegNet network model converge.

实施例三:Embodiment three:

基于实施例一,本发明实施例提供了一种气象雷达杂波分类识别装置,包括处理器及存储介质;Based on Embodiment 1, the embodiment of the present invention provides a weather radar clutter classification and identification device, including a processor and a storage medium;

存储介质用于存储指令;The storage medium is used to store instructions;

处理器用于根据指令进行操作以执行根据上述方法的步骤。The processor is configured to operate in accordance with the instructions to perform the steps according to the methods described above.

实施例四:Embodiment four:

基于实施例一,本发明实施例提供了计算机可读存储介质,其上存储有计算机程序,该程序被处理器执行时实现上述方法的步骤。Based on the first embodiment, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

本领域内的技术人员应明白,本申请的实施例可提供为方法、系统、或计算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,在不脱离本发明技术原理的前提下,还可以做出若干改进和变形,这些改进和变形也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made. It should also be regarded as the protection scope of the present invention.

Claims (10)

1. A meteorological radar clutter classification and identification method is characterized by comprising the following steps:
acquiring and preprocessing tested meteorological radar data;
inputting the preprocessed tested meteorological radar data into a trained SegNet network model to obtain a classification recognition result;
wherein the training process of the SegNet network model comprises the following steps:
acquiring and preprocessing trained meteorological radar data; the meteorological radar data comprise radar reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase;
integrating the preprocessed meteorological radar data into a corresponding training data set based on the data type;
respectively calculating the standard deviation of a training data set corresponding to the radar reflectivity and the differential phase as texture data of the radar reflectivity and the differential phase;
processing training data and texture data in the training data set by adopting a fuzzy logic algorithm to obtain a particle phase state type label set;
initializing a SegNet network model, and performing iterative training based on a training data set and a label set until a preset maximum iteration number is reached or a weight parameter of the SegNet network model is converged.
2. The weather radar clutter classification and identification method according to claim 1, wherein the preprocessing comprises data cleaning and scale expansion;
the data cleaning comprises searching for NaN data values and radar reflectivity smaller than zero in meteorological radar data, and respectively setting the NaN data values and the radar reflectivity smaller than zero;
and the scale expansion comprises the step of expanding the meteorological radar data in a mode of upper left alignment and lower right zero padding.
3. The method for classifying and identifying weather radar clutter according to claim 1, wherein the calculating the standard deviation of the training data set corresponding to radar reflectivity and differential phase respectively comprises:
traversing a training data set corresponding to the radar reflectivity, and calculating the standard deviation S of the radar reflectivity within 1km range D (Z H ):
Figure FDA0003858997620000021
Figure FDA0003858997620000022
In the formula, m (Z) H ) Is radar reflectivity Z in the range of 1km H Average value of (1), n Z The number of data points of radar reflectivity within a range of 1 km;
traversing a training data set corresponding to the differential phase, and calculating the standard deviation of the differential phase within the range of 2km
Figure FDA0003858997620000023
Figure FDA0003858997620000024
In the formula, m (phi) DP ) Is differential phase phi in the range of 2km DP Average value of (3), n φ The number of data points of differential phase in the range of 2 km.
4. The weather radar clutter classification and identification method according to claim 1, wherein the processing the training data set and the texture data by the fuzzy logic algorithm to obtain the tag set of the particle phase type comprises:
adopting a trapezoidal function as a membership function of a fuzzy logic algorithm, carrying out fuzzy logic operation by taking training data and texture data in a training data set as input parameters, and obtaining membership corresponding to a particle phase type as fuzzy logic output; the trapezoidal function is:
Figure FDA0003858997620000025
in the formula, X 1 、X 2 、X 3 、X 4 Is a threshold parameter of a trapezoidal function, P (j) (x i ) The membership degree of the jth input parameter to the ith particle phase state type;
and (3) performing defuzzification on the fuzzy logic output by adopting a weighted average judgment method to obtain a membership integrated value corresponding to the particle phase type:
Figure FDA0003858997620000031
in the formula, S i Membership level integration value, W, of ith particle phase type for jth input parameter ij The weight coefficient of the jth input parameter of the ith particle phase type is obtained, and J is the number of the input parameters;
if the membership integrated value of the input parameter is 0, the particle phase type corresponding to the input parameter is no meteorological echo, and the label of the particle phase type is set to be 0;
if the membership integrated value of the input parameter is greater than the set threshold, the particle phase type corresponding to the input parameter is a clutter, and the label of the particle phase type is set to be 1;
if the membership integration value of the input parameter is smaller than the set threshold and larger than 0, the particle phase type corresponding to the input parameter is other, and the label of the particle phase type is set to be 2;
and constructing a tag set of the particle phase type according to the tags of the input parameters.
5. The weather radar clutter classification and identification method according to claim 1, wherein the iterative training based on the training data set and the tag set comprises:
an Encoder network downsampling training data based on a SegNet network model to obtain data characteristics;
sampling characteristic data on a Decoder network based on a SegNet network model to obtain a prediction result;
and calculating the loss of the prediction result and the label thereof based on the cross entropy loss function, and updating the weight parameter of the SegNet network model according to the loss back propagation.
6. The weather radar clutter classification and identification method according to claim 5, wherein the Encoder network comprises:
a first layer consisting of two 64 x 3 convolutions and maximum pooled downsampling in sequence;
a second layer consisting of, in order, two 128 x 3 convolutions and maximum pooled downsampling;
the third layer consists of three convolutions of 256 multiplied by 3 and the maximum pooled downsampling in sequence;
a fourth layer consisting of three 512 x 3 convolutions and maximum pooled downsampling in sequence;
a fifth layer consisting of three 5212 × 3 × 3 convolutions and maximum pooled downsampling in sequence;
the Decoder network includes:
a first layer consisting of, in order, inverse max-pooling upsampling and three 521 × 3 × 3 convolutions;
a second layer consisting of, in order, inverse maximal pooled upsampling and three 521 × 3 × 3 convolutions;
the third layer consists of inverse maximum pooling upsampling and two convolutions of 256 multiplied by 3 in turn;
a fourth layer consisting of inverse maximal pooled upsampling and two 128 x 3 convolutions in sequence;
the fifth layer, in turn, consists of inverse max pooling upsampling and two 64 x 3 convolutions.
7. The weather radar clutter classification and identification method according to claim 5, wherein in the iterative training process, the Pixel Accuracy is used as an Accuracy evaluation index to obtain the proximity of the prediction result and the tag.
8. A weather radar clutter classification and identification device is characterized in that the device comprises:
the preprocessing module is used for acquiring and preprocessing the tested meteorological radar data;
the classification identification module is used for inputting the preprocessed tested meteorological radar data into the trained SegNet network model to obtain a classification identification result;
a model training module to:
acquiring and preprocessing trained meteorological radar data; the meteorological radar data comprise radar reflectivity, differential propagation phase shift rate, correlation coefficient and differential phase;
integrating the preprocessed meteorological radar data into a corresponding training data set based on the data type;
respectively calculating the standard deviation of a training data set corresponding to the radar reflectivity and the differential phase as texture data of the radar reflectivity and the differential phase;
processing training data and texture data in the training data set by adopting a fuzzy logic algorithm to obtain a particle phase state type label set;
initializing a SegNet network model, and performing iterative training based on a training data set and a label set until a preset maximum iteration number is reached or a weight parameter of the SegNet network model is converged.
9. A meteorological radar clutter classification and identification device is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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