逆合成孔径成像雷达隐身目标零样本识别

doi:10.12305/j.issn.1001-506X.2023.10.15

Abstract

Abstract:

For the zero-shot learning and recognition problem of stealth targets for inverse synthetic aperture imaging radar (ISAR), the existed algorithms cannot recognize the new category of targets that have not appeared in the training process. A target learning and recognition method with zero-shot learning (ZSL) is proposed for solving the problem above. Firstly, based on three dimensional grid physical model of the aircraft-class dynamic target, the FEKO electromagnetic field simulation software programming is used to realize the ISAR-visible source target image data generation. Secondly, on this basis, the text semantic feature expression of detail attributes for different aircraft-class targets is formed. In the novel-type proposed algorithm's network model, two variation autoencoders are used to generate image and semantic features respectively, so that the network can learn the modal's invariant feature expression. The network is trained with visible category data to obtain the model that can generate image features based on semantic information. The training recognition process adopts the targets of the new category which are unknown and invisible for the proposed learning model. The unknown and invisible image feature information of the new target is generated from the invisible and unknown text semantic of the new category, which supports the recognition for the new targets which are unknown and invisible. The accuracy rate of the recognition for the new category targets is up to 75% according to the relevant statistic result, which means an important military and strategic significance.

Key words: inverse synthetic aperture imaging radar (ISAR), zero-shot learning (ZSL), stealth target

CLC Number:

TN957.52

Chunhua ZHOU, Weiwei WEI, Xuecheng ZHANG, Xin ZHENG, Mianzhi CHENG. Zero-shot identification for stealth target by inverse synthetic aperture imaging radar[J]. Systems Engineering and Electronics, 2023, 45(10): 3116-3121.

Figures/Tables 8

Fig.1

Fig.2

Table 1

Table 2

Table 3

Fig.3

Table 4

Fig.4

References 19

1	ALAMRI F, DUTTA A. Multi-head self-attention via vision transformer for zero-shot learning[EB/OL]. [2021-07-30]. https://arxiv.org/abs/2108.00045.
2	JANIK M, GARD N, HILSMANN A, et al. Zero in on shape: a generic 2D-3D instance similarity metric learned from synthetic data[C]//Proc. of the IEEE International Conference on Image Processing, 2021: 2638-2642.
3	MICHELE B, BOULCH A, PUY G, et al. Generative zero-shot learning for semantic segmentation of 3D point cloud[EB/OL]. [2021-08-13]. https://arxiv.org/abs/2108.06230.
4	HE T, GAO L L, SONG J K, et al. 2021. Semantic compositional learning for low-shot scene graph generation[EB/OL]. [2021-08-19]. https://arxiv.org/abs/2108.08600.
5	BIANCHI F, ATTANASIO G, PISONI R, et al. Contrastive language-image pre-training for the Italian language[EB/OL]. [2021-08-19]. https://arxiv.org/abs/2108.08688.
6	NARAYAN S, GUPTA A, KHAN S, et al. Discriminative region-based multi-label zero-shot learning[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2021: 8731-8740.
7	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets. Advances in neural information processing systems[EB/OL]. [2014-06-10]. https://arxiv.org/abs/1406.2661.
8	KINGMA D P, WELLING M. Auto-encoding variational Bayes [EB/OL]. [2013-12-20]. https://arxiv.org/abs/1312.6114.
9	FELIX R, REID I, CARNEIRO G. Multi-modal cycle-consis-tent generalized zero-shot learning[C]//Proc. of the European Conference on Computer Vision, 2018: 21-37.
10	ISOLA P, ZHU J Y, ZHOU T, et al. Image-to-image translation with conditional adversarial networks[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1125-1134.
11	KODIROV E, XIANG T, GONG S G. Semantic autoencoder for zero-shot learning[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.
12	LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2009: 951-958.
13	XIAN Y Q , LAMPERT C H , SCHIELE B , et al. Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2018, 41 (9): 2251- 2265.
14	AKATA Z , PERRONNIN F , HARCHAOUI Z , et al. Labe-lembedding for image classification[J]. IEEE Trans.on Pattern Analysis and Machine Intelligence, 2015, 38 (7): 1425- 1438.
15	PATTERSON G, HAYS J. Sun attribute database: discovering, annotating, and recognizing scene attributes[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2751-2758.
16	ZHANG L, XIANG T, GONG S G. Learning a deep embedding model for zero-shot learning[C]//Proce. of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2021-2030.
17	ZHU Y Z, ELHOSEINY M, LIU B C, et al. A generative adversarial approach for zero-shot learning from noisy texts[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 1004-1013.
18	VERMA V K, ARORA G, MISHRA A, et al. Generalized zero-shot learning via synthesized examples[C]//Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4281-4289.
19	ATZMON Y, CHECHIK G. Adaptive confidence smoothing for generalized zero-shot learning[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 11671-11680.

方法	AwA1	AwA2	CUB	SUN
ALE^[14]	27.5	23.9	34.4	26.3
DEM^[16]	47.3	45.1	29.2	35.6
GAZSL^[17]	31.4	50.3	34.3	26.7
SE^[18]	61.5	62.8	46.7	34.9
COSMO^[19]	63.6	-	50.2	41.0
本文算法	67.6	68.2	55.0	41.4

属性	内容(单位)	描述
机身长度	数值/m	飞机的纵向长度
翼展	数值/m	飞机机翼总展开长度
是否有翼尖小翼	布尔值	机翼局部特征描述
尾翼	布尔值	尾翼局部特征描述
机翼面积	数值/m²	机翼展开总面积
高度	数字/m	最高点至最低点的距离差
展弦比	比率/%	翼展和翼弦长度比值
发动机数	数值/个	总引擎数目
机翼形状	选项值(后掠翼、三角翼、梯形翼、菱形翼)	飞机机翼俯视图形状
推进动力	选项值(螺旋桨, 喷气机)	飞机引擎动力来源
机翼数量	数值/个	飞机机翼总数目
起落架数	数值/个	飞机起落架总数目
主起落架轮子数	数值/个	主起落架轮子总数目
垂直稳定器数量	数值/个	垂直稳定器总数目
飞机类型	选项值(民航客机、民航货机、战斗机、轰炸机、军用运输机)	飞机设计用途分类
机翼挂架数目	数值/个	机翼部分炸弹挂载点
机腹挂架数目	数值/个	机腹部分炸弹挂载点

方法		F22	TU-160
传统方法	ALE^[14]	32.4	27.9
传统方法	DEM^[16]	41.3	35.7
生成方法	VAE	59.3	41.1
	GAN	72.4	49.9
	本文方法	86.0	63.1

[1]	WANG Zhuo, ZHENG Xuehe, CHANG Xiaolan. High maneuvering stealth target detection algorithm based on IFM [J]. Systems Engineering and Electronics, 2019, 41(5): 1000-1006.
[2]	WANG Zhuo, ZHENG Xuehe, CHANG Xiaolan. High-speed stealth target detection algorithm based on rotating and dimension reduction [J]. Systems Engineering and Electronics, 2019, 41(1): 14-20.
[3]	WANG Zhuo, ZHENG Xuehe, CHANG Xiaolan. Stealth target detection algorithm based on blind acceleration estimation [J]. Systems Engineering and Electronics, 2017, 39(8): 1691-1696.

Zero-shot identification for stealth target by inverse synthetic aperture imaging radar

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 19

Related Articles 3

Recommended Articles

Metrics

Comments

类别	数目
1	100
2	100
3	100
4	100
5	100
6	100
7	100
8	100
9	100
10	100
11	100
12	100
13	100
14	100
15	100
16	100
17	100
18	100