小样本条件下SCGAN+CNN低分辨雷达目标一步识别算法

doi:10.3969/j.issn.1001-506X.2020.01.10

Abstract

Abstract:

The traditional low-resolution radar target recognition technology has the difficulty in improving the recognition rate and insufficient generalization because of using a two-step recognition algorithm based on feature extraction and target classification. A low-resolution radar target one-step recognition algorithm based on strengthening condition generative adversarial network (SCGAN)+convolution neural network (CNN) is put forward. CNN is used in the algorithm to automatically obtain the sampling data deep essence characteristics to achieve the low-resolution radar target one-step recognition. In order to improve the recognition rate with limited training data, CGAN theory is used to improve the coverage of sampling in feature space. Moreover, an SCGAN model is proposed to generate samples with unmixed distribution by adding mixed punishment to the discriminator. CNN can better identify radar targets based on SCGAN. The numerical simulations have demonstrated the effectiveness of the low-resolution radar target one-step recognition algorithm and the recognition algorithm based SCGAN+CNN.

Key words: low-resolution radar target recognition, one-step recognition algorithm, convolutional neural network (CNN), generative adversarial network

CLC Number:

TN959.1

Kefan ZHU, Jiegui WANG. Low-resolution radar target one-step recognition algorithm based on SCGAN+CNN with a limited training data[J]. Systems Engineering and Electronics, 2020, 42(1): 67-75.

Figures/Tables 18

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实验号	生成样本数	识别率/%
1	0	83.3
2	200	84.7
3	400	85.3
4	600	86.9

识别方法	训练时间/s	识别时间/s
SCGAN+CNN	2.225	0.057
CGAN+CNN	2.343	0.059
CNN	1.142	0.058

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Low-resolution radar target one-step recognition algorithm based on SCGAN+CNN with a limited training data

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