Electromagnetic target classification method based on knowledge vector embedding

A technology of target classification and target category, which is applied in the field of communication, can solve problems such as hard judgments that cannot reflect the relationship between the categories to which samples belong, and achieve the effect of wide application range and strong applicability

Active Publication Date: 2022-04-29
中国人民解放军32802部队
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AI Technical Summary

Problems solved by technology

The present invention converts the hard decision of the traditional classification neural network represented by the convolutional neural network into a soft decision for calculating the similarity between vec...

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  • Electromagnetic target classification method based on knowledge vector embedding
  • Electromagnetic target classification method based on knowledge vector embedding
  • Electromagnetic target classification method based on knowledge vector embedding

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Embodiment Construction

[0045] In order to better understand the contents of the present invention, an example is given here.

[0046] figure 1 It is the realization flowchart of the method of the present invention; figure 2 It is the relationship structure diagram of the nodes corresponding to the categories in this implementation; image 3 is the result graph obtained after dimensionality reduction of the category embedding vector in this implementation; Figure 4 It is the result graph obtained after dimensionality reduction of the sample embedding vector in this implementation.

[0047] The invention discloses a method for classifying electromagnetic targets using knowledge vector embedding. The specific steps include: using known electromagnetic target information data to establish a graph structure of electromagnetic targets, the graph structure includes graph nodes and relationships, and the graph nodes use is used to represent the known electromagnetic target categories, and the relations...

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Abstract

The invention discloses an electromagnetic target classification method based on knowledge vector embedding, and the method comprises the steps: building a graph structure of an electromagnetic target through the data of known electromagnetic target information, and carrying out the embedded vector representation of a graph node corresponding to each electromagnetic target type based on a graph neural network; the method comprises the following steps: acquiring an electromagnetic target signal, performing short-time Fourier transform on electromagnetic target data to obtain time-frequency data of the electromagnetic target data, and preprocessing the time-frequency data to serve as a sample for training a convolutional neural network; constructing a convolutional neural network, training the convolutional neural network based on a result represented by the embedded vector of the graph node corresponding to the electromagnetic target category, and finally obtaining a reference vector for subsequently classifying and identifying the acquired electromagnetic target signal; and the acquired electromagnetic target signal is classified and identified by using the obtained reference vector. The method is high in applicability, the category relationship knowledge is combined into network training, and the defect that a traditional classification network is only suitable for recognizing categories appearing in a training set is overcome.

Description

technical field [0001] The invention relates to the fields of communication and artificial intelligence, and in particular to an electromagnetic target classification method using knowledge vector embedding. Background technique [0002] In the knowledge graph, the knowledge of the graph structure mainly includes node knowledge and relational knowledge. A node can represent a known category, and the relationship is the degree of connection between each category, which can intuitively reflect the closeness of the relationship between nodes. After embedding the graph structure into a vector form, the vector representation of each node can be Reflect knowledge of relationships between nodes. In recent years, due to the development of the graph neural network, it can effectively use the attributes and mutual relationships of the nodes in the graph structure, and rely on their mutual relationships for modeling, and embed the nodes as vectors to provide a computable data format f...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/12
Inventor 杨健周航刘杰鲍雁飞房珊瑶
Owner 中国人民解放军32802部队
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