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Radar target identification method based on convolutional neural network and Bert

A convolutional neural network and radar target technology, applied in the field of radar target recognition, can solve the problems of ignoring sequence correlation, feature redundancy, and inability to efficiently use prior information, and achieve the effect of improving recognition accuracy and good recognition.

Active Publication Date: 2021-05-07
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

The time-domain segmentation method used in the first method makes the features highly redundant and cannot efficiently use prior information. The latter two methods can effectively extract the overall envelope information of HRRP data, but ignore the sequence correlation.

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  • Radar target identification method based on convolutional neural network and Bert
  • Radar target identification method based on convolutional neural network and Bert
  • Radar target identification method based on convolutional neural network and Bert

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Embodiment Construction

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0054] refer to figure 1 , showing the step flow chart of the radar high-resolution range image recognition method based on the convolutional neural network and Bert according to the embodiment of the present invention, which includes the following steps:

[0055] S1, collect data and divide the training set and test set, sample each category through the HRRP data collected by radar, select the training set and test set respectively, and then merg...

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Abstract

The invention discloses a radar target recognition method based on a convolutional neural network and Bert. The method comprises the following steps: S1, collecting data, dividing the data into a training set and a test set, and carrying out the intensity normalization and gravity center alignment of the data; S2, inputting the processed HRRP sample into a CNN module, and performing feature extraction on the processed sample by using the CNN; S3, using Bert to process the effective features extracted by the CNN, and extracting deeper features; S4, constructing a classifier, classifying an HRRP target, retaining more effective features for the output of Bert by using the attention mechanism again, and finally classifying the output of the network by adopting softmax; and S5, sending the HRRP test set processed in step S1 into the models trained in the steps S2, S3 and S4 for testing.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, and in particular relates to a radar target recognition method based on a convolutional neural network and Bert. Background technique [0002] Radar target recognition technology is a technology that uses radar and computer to identify and classify targets. Through the analysis of target characteristic information such as amplitude, phase, spectrum and polarization in radar echoes, the model is used to calculate the size, shape and weight of targets. And the physical characteristic parameters of the surface layer, and finally determine the model based on a large amount of training data, and identify and classify in the classifier. One-dimensional high-resolution range profile (HRRP) is the echo of high-resolution broadband radar to the target. The range resolution of high-resolution broadband radar is much smaller than the size of the target, and its radar target echo signals of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/88
CPCG01S13/88Y02A90/10
Inventor 潘勉唐三鼎吕帅帅李训根陈晴方笑海张杰
Owner HANGZHOU DIANZI UNIV
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