Feature-level fusion recognition method of radar jamming signal based on deep convolutional neural network

A convolutional neural network and radar jamming technology, applied in the field of radar jamming signal feature-level fusion recognition, can solve the problems of features susceptible to noise, redundancy, etc., to improve robustness and robustness, improve fault tolerance and robustness Stickiness, the effect of improving the accuracy of interference identification

Active Publication Date: 2022-07-15
HARBIN INST OF TECH
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Problems solved by technology

[0005] Aiming at the problem that the characteristic parameters of the current radar jamming signal rely on manual extraction, are easily affected by noise and have feature redundancy, the present invention provides a radar jamming signal feature-level fusion recognition method based on a deep convolutional neural network

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  • Feature-level fusion recognition method of radar jamming signal based on deep convolutional neural network
  • Feature-level fusion recognition method of radar jamming signal based on deep convolutional neural network

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specific Embodiment approach 1

[0042] Specific implementation mode 1. Combination figure 1 and figure 2 As shown, the present invention provides a feature-level fusion identification method of radar jamming signals based on a deep convolutional neural network, including establishing a radar jamming time domain data set, and analyzing the radar jamming time domain data in the radar jamming time domain data set with two Extract feature vectors in different forms, and then fuse the two extracted feature vectors in series; use the fused feature vectors to train a support vector machine to obtain a trained radar interference signal feature-level fusion recognition model;

[0043] The two extracted feature vectors include: a feature vector extracted by a one-dimensional convolutional neural network and an expert feature vector extracted manually;

[0044] Or the feature vector extracted by one-dimensional convolutional neural network and the time-frequency domain feature vector extracted by deep convolutional n...

specific Embodiment 1

[0049] combine figure 1 As shown, for the feature vector extracted by one-dimensional convolutional neural network and the expert feature vector extracted manually, the process of obtaining the feature-level fusion recognition model of radar interference signal includes:

[0050] The artificial feature parameter extraction is performed on the radar jamming time domain data, and the obtained expert feature vectors include the time domain moment skewness, the time domain moment kurtosis, the time domain signal envelope fluctuation, the mean value of the time domain jamming signal and the variance of the time domain jamming signal. ;

[0051] At the same time, the radar interference time domain data is divided into training set, validation set and test set;

[0052] Using a one-dimensional convolutional neural network to extract the training sample feature vector from the data in the training set, perform PCA processing on the training sample feature vector, and then fuse the PC...

specific Embodiment 2

[0070] combine figure 2 As shown, for the feature vector extracted by the one-dimensional convolutional neural network and the time-frequency domain feature vector extracted by the deep convolutional neural network, the process of obtaining the feature-level fusion recognition model of the radar interference signal includes:

[0071] Divide the radar interference time domain data into time domain training set, time domain validation set and time domain test set;

[0072] Using a one-dimensional convolutional neural network to extract the time-domain training sample feature vector from the data in the time-domain training set;

[0073] At the same time, the time-frequency transform of the radar jamming time domain data is performed to obtain the radar jamming time-frequency domain data, and the radar jamming time-frequency domain data is divided into a time-frequency domain training set, a time-frequency domain verification set and a time-frequency domain test set;

[0074] U...

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Abstract

The invention discloses a feature-level fusion identification method of radar jamming signals based on a deep convolutional neural network, which belongs to the field of radar jamming signal identification. The present invention aims at the problem that the characteristic parameters of the current radar interference signal rely on manual extraction, which is easily affected by noise and features redundancy. Including establishing a radar jamming time domain data set, extracting feature vectors from the radar jamming time domain data in the radar jamming time domain dataset in two different forms, and then merging the two extracted feature vectors in series; using the fused feature vector training Support vector machine to obtain the trained radar interference signal feature-level fusion recognition model; input the collected test samples into the recognition model to obtain the radar interference signal recognition result. The invention uses CNN to extract the deep features of the radar interference signal, and designs different radar interference signal data fusion models at the feature level, so that the signal identification is free from the influence of noise, and the feature redundancy phenomenon is eliminated at the same time.

Description

technical field [0001] The invention relates to a feature-level fusion identification method of radar interference signals based on a deep convolutional neural network, and belongs to the field of radar interference signal identification. Background technique [0002] With the increasing complexity of the electromagnetic environment and the increasing number of interference patterns, in order to ensure that the radar can still play an effective tracking and detection role in the extremely harsh electromagnetic environment, it is necessary to greatly improve the anti-interference performance of the interference. Anti-jamming capability of radar equipment. The key and foundation of radar anti-jamming technology is to classify and identify radar jamming signals efficiently. Therefore, it is an urgent problem to design an interference signal recognition model with high recognition accuracy and robustness. [0003] One of the core steps in the identification process of radar ja...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/254G06F18/253
Inventor 邵广庆陈雨时于雷位寅生李迎春
Owner HARBIN INST OF TECH
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