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Signal recognition method of passive radar external radiation source based on transfer learning

A passive radar, external radiation source technology, applied in radio wave measurement systems, instruments, etc., can solve problems such as insufficient results and inflexible models, and save training time.

Active Publication Date: 2022-04-08
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

When traditional machine learning deals with tasks such as data distribution, dimensionality, and model output changes, the model is not flexible enough and the results are not good enough, while transfer learning relaxes these assumptions

Method used

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  • Signal recognition method of passive radar external radiation source based on transfer learning
  • Signal recognition method of passive radar external radiation source based on transfer learning
  • Signal recognition method of passive radar external radiation source based on transfer learning

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Embodiment Construction

[0024] The present invention aims to design a migration learning method for external radiation source signal recognition, which can solve the migration problem of models obtained by training signals with different sampling rates. This method is based on model-based parameter migration, which migrates the model and parameters trained on a sampling frequency dataset (source domain) to another sampling frequency dataset (target domain), requiring only a small amount of labeled target domain data. , after a short period of training, the recognition model of the target domain can be obtained. This method is independent of the size relationship between the sampling frequency of the source domain and the target domain.

[0025] (1) Model structure

[0026] The realization model of the present invention is as figure 1 shown.

[0027] Firstly, a model is trained on a sampling frequency dataset as the basic network, and then the model and parameters of the basic network are directly...

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Abstract

The invention belongs to the field of radar and communication signal identification, and aims to provide a migration learning method for external radiation source signal identification, which can solve the problem of migration of models obtained through signal training with different sampling rates. This method is independent of the size relationship between the sampling frequency of the source domain and the target domain. For this reason, the technical scheme adopted in the present invention is, based on the transfer learning method for identifying the passive radar external radiation source signal, firstly, a model is obtained by training on a sampling frequency data set as the basic network, and then the model and parameters of the basic network are directly transferred Go to the target domain of the data set with different sampling frequencies, and do fine-tuning training. The basic network uses a neural network model with 3 layers of convolution and 2 layers of fully connected layers. The invention is mainly applied to radar and communication signal identification occasions.

Description

technical field [0001] The invention belongs to the field of radar and communication signal identification and the field of transfer learning. Based on the trained deep learning model, a transfer learning-based signal recognition method for passive radar external emitters is designed. Background technique [0002] The complex and heterogeneous electromagnetic environment brings huge challenges to signal processing. It is urgent to develop technologies for special applications of military-civilian integration, public systems and dedicated systems, so as to effectively improve spectrum utilization efficiency, improve the environment, and coexist cooperatively. Therefore, the future radar system design must start from the perspective of improving the utilization of spectrum resources. Passive radar, waveform diversity, bionic design and cognitive methods are effective methods to solve spectrum congestion. [0003] Passive radar (also known as passive radar, external radiation ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/02
CPCG01S7/021
Inventor 汪清杜攀非刘文斌贺爽
Owner TIANJIN UNIV
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